Presentation 3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "66de36dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e808bca1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler, PolynomialFeatures\n",
    "from sklearn.feature_selection import SelectKBest, chi2, mutual_info_classif\n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV, train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import BaggingClassifier, RandomForestClassifier\n",
    "from scipy.stats import uniform, randint\n",
    "\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix # accuracy and confusion matrix\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score, classification_report # precision, recall, and f1\n",
    "from sklearn.metrics import roc_auc_score # roc and auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "98b31b4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type_school</th>\n",
       "      <th>school_accreditation</th>\n",
       "      <th>gender</th>\n",
       "      <th>interest</th>\n",
       "      <th>residence</th>\n",
       "      <th>parent_age</th>\n",
       "      <th>parent_salary</th>\n",
       "      <th>house_area</th>\n",
       "      <th>average_grades</th>\n",
       "      <th>parent_was_in_college</th>\n",
       "      <th>in_college</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Academic</td>\n",
       "      <td>A</td>\n",
       "      <td>Male</td>\n",
       "      <td>Less Interested</td>\n",
       "      <td>Urban</td>\n",
       "      <td>56</td>\n",
       "      <td>6950000</td>\n",
       "      <td>83.0</td>\n",
       "      <td>84.09</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Academic</td>\n",
       "      <td>A</td>\n",
       "      <td>Male</td>\n",
       "      <td>Less Interested</td>\n",
       "      <td>Urban</td>\n",
       "      <td>57</td>\n",
       "      <td>4410000</td>\n",
       "      <td>76.8</td>\n",
       "      <td>86.91</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Academic</td>\n",
       "      <td>B</td>\n",
       "      <td>Female</td>\n",
       "      <td>Very Interested</td>\n",
       "      <td>Urban</td>\n",
       "      <td>50</td>\n",
       "      <td>6500000</td>\n",
       "      <td>80.6</td>\n",
       "      <td>87.43</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Vocational</td>\n",
       "      <td>B</td>\n",
       "      <td>Male</td>\n",
       "      <td>Very Interested</td>\n",
       "      <td>Rural</td>\n",
       "      <td>49</td>\n",
       "      <td>6600000</td>\n",
       "      <td>78.2</td>\n",
       "      <td>82.12</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Academic</td>\n",
       "      <td>A</td>\n",
       "      <td>Female</td>\n",
       "      <td>Very Interested</td>\n",
       "      <td>Urban</td>\n",
       "      <td>57</td>\n",
       "      <td>5250000</td>\n",
       "      <td>75.1</td>\n",
       "      <td>86.79</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>Vocational</td>\n",
       "      <td>A</td>\n",
       "      <td>Female</td>\n",
       "      <td>Very Interested</td>\n",
       "      <td>Rural</td>\n",
       "      <td>49</td>\n",
       "      <td>7420000</td>\n",
       "      <td>63.6</td>\n",
       "      <td>85.99</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>Academic</td>\n",
       "      <td>B</td>\n",
       "      <td>Female</td>\n",
       "      <td>Less Interested</td>\n",
       "      <td>Rural</td>\n",
       "      <td>51</td>\n",
       "      <td>7480000</td>\n",
       "      <td>84.3</td>\n",
       "      <td>89.72</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>Vocational</td>\n",
       "      <td>A</td>\n",
       "      <td>Male</td>\n",
       "      <td>Less Interested</td>\n",
       "      <td>Urban</td>\n",
       "      <td>49</td>\n",
       "      <td>5550000</td>\n",
       "      <td>75.2</td>\n",
       "      <td>79.56</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>Academic</td>\n",
       "      <td>B</td>\n",
       "      <td>Male</td>\n",
       "      <td>Uncertain</td>\n",
       "      <td>Rural</td>\n",
       "      <td>53</td>\n",
       "      <td>5840000</td>\n",
       "      <td>105.8</td>\n",
       "      <td>87.18</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>Academic</td>\n",
       "      <td>B</td>\n",
       "      <td>Male</td>\n",
       "      <td>Quiet Interested</td>\n",
       "      <td>Urban</td>\n",
       "      <td>50</td>\n",
       "      <td>2940000</td>\n",
       "      <td>69.1</td>\n",
       "      <td>86.13</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    type_school school_accreditation  gender          interest residence  \\\n",
       "0      Academic                    A    Male   Less Interested     Urban   \n",
       "1      Academic                    A    Male   Less Interested     Urban   \n",
       "2      Academic                    B  Female   Very Interested     Urban   \n",
       "3    Vocational                    B    Male   Very Interested     Rural   \n",
       "4      Academic                    A  Female   Very Interested     Urban   \n",
       "..          ...                  ...     ...               ...       ...   \n",
       "995  Vocational                    A  Female   Very Interested     Rural   \n",
       "996    Academic                    B  Female   Less Interested     Rural   \n",
       "997  Vocational                    A    Male   Less Interested     Urban   \n",
       "998    Academic                    B    Male         Uncertain     Rural   \n",
       "999    Academic                    B    Male  Quiet Interested     Urban   \n",
       "\n",
       "     parent_age  parent_salary  house_area  average_grades  \\\n",
       "0            56        6950000        83.0           84.09   \n",
       "1            57        4410000        76.8           86.91   \n",
       "2            50        6500000        80.6           87.43   \n",
       "3            49        6600000        78.2           82.12   \n",
       "4            57        5250000        75.1           86.79   \n",
       "..          ...            ...         ...             ...   \n",
       "995          49        7420000        63.6           85.99   \n",
       "996          51        7480000        84.3           89.72   \n",
       "997          49        5550000        75.2           79.56   \n",
       "998          53        5840000       105.8           87.18   \n",
       "999          50        2940000        69.1           86.13   \n",
       "\n",
       "     parent_was_in_college  in_college  \n",
       "0                    False        True  \n",
       "1                    False        True  \n",
       "2                    False        True  \n",
       "3                     True        True  \n",
       "4                    False       False  \n",
       "..                     ...         ...  \n",
       "995                   True        True  \n",
       "996                   True        True  \n",
       "997                  False        True  \n",
       "998                   True        True  \n",
       "999                   True       False  \n",
       "\n",
       "[1000 rows x 11 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college = pd.read_csv(\"data.csv\")\n",
    "college"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "31dc094e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type_school               object\n",
       "school_accreditation      object\n",
       "gender                    object\n",
       "interest                  object\n",
       "residence                 object\n",
       "parent_age                 int64\n",
       "parent_salary              int64\n",
       "house_area               float64\n",
       "average_grades           float64\n",
       "parent_was_in_college       bool\n",
       "in_college                  bool\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d3234535",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type_school              0\n",
       "school_accreditation     0\n",
       "gender                   0\n",
       "interest                 0\n",
       "residence                0\n",
       "parent_age               0\n",
       "parent_salary            0\n",
       "house_area               0\n",
       "average_grades           0\n",
       "parent_was_in_college    0\n",
       "in_college               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7ebc5cd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    519\n",
       "A    481\n",
       "Name: school_accreditation, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college['school_accreditation'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "85097e5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Very Interested     324\n",
       "Uncertain           261\n",
       "Less Interested     229\n",
       "Quiet Interested    100\n",
       "Not Interested       86\n",
       "Name: interest, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college['interest'].value_counts() #How interested are students if they go to college"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "56dda098",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>parent_age</th>\n",
       "      <th>parent_salary</th>\n",
       "      <th>house_area</th>\n",
       "      <th>average_grades</th>\n",
       "      <th>parent_was_in_college</th>\n",
       "      <th>in_college</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>parent_age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.286418</td>\n",
       "      <td>0.107060</td>\n",
       "      <td>-0.006077</td>\n",
       "      <td>-0.438322</td>\n",
       "      <td>0.042873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parent_salary</th>\n",
       "      <td>-0.286418</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.138357</td>\n",
       "      <td>0.306712</td>\n",
       "      <td>0.106487</td>\n",
       "      <td>0.475693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>house_area</th>\n",
       "      <td>0.107060</td>\n",
       "      <td>0.138357</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.409565</td>\n",
       "      <td>0.145186</td>\n",
       "      <td>0.467267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_grades</th>\n",
       "      <td>-0.006077</td>\n",
       "      <td>0.306712</td>\n",
       "      <td>0.409565</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.236713</td>\n",
       "      <td>0.505133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parent_was_in_college</th>\n",
       "      <td>-0.438322</td>\n",
       "      <td>0.106487</td>\n",
       "      <td>0.145186</td>\n",
       "      <td>0.236713</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.048038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>in_college</th>\n",
       "      <td>0.042873</td>\n",
       "      <td>0.475693</td>\n",
       "      <td>0.467267</td>\n",
       "      <td>0.505133</td>\n",
       "      <td>0.048038</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       parent_age  parent_salary  house_area  average_grades  \\\n",
       "parent_age               1.000000      -0.286418    0.107060       -0.006077   \n",
       "parent_salary           -0.286418       1.000000    0.138357        0.306712   \n",
       "house_area               0.107060       0.138357    1.000000        0.409565   \n",
       "average_grades          -0.006077       0.306712    0.409565        1.000000   \n",
       "parent_was_in_college   -0.438322       0.106487    0.145186        0.236713   \n",
       "in_college               0.042873       0.475693    0.467267        0.505133   \n",
       "\n",
       "                       parent_was_in_college  in_college  \n",
       "parent_age                         -0.438322    0.042873  \n",
       "parent_salary                       0.106487    0.475693  \n",
       "house_area                          0.145186    0.467267  \n",
       "average_grades                      0.236713    0.505133  \n",
       "parent_was_in_college               1.000000    0.048038  \n",
       "in_college                          0.048038    1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "college.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0307223d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:ylabel='Density'>, <AxesSubplot:ylabel='Density'>,\n",
       "        <AxesSubplot:ylabel='Density'>],\n",
       "       [<AxesSubplot:ylabel='Density'>, <AxesSubplot:ylabel='Density'>,\n",
       "        <AxesSubplot:ylabel='Density'>],\n",
       "       [<AxesSubplot:ylabel='Density'>, <AxesSubplot:ylabel='Density'>,\n",
       "        <AxesSubplot:ylabel='Density'>],\n",
       "       [<AxesSubplot:ylabel='Density'>, <AxesSubplot:ylabel='Density'>,\n",
       "        <AxesSubplot:ylabel='Density'>]], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1440x720 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "college.plot(kind='density', subplots=True, layout=(4,3), sharex=False, \n",
    "                     sharey=False,fontsize=12, figsize=(20,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "157497f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "hovertemplate": "in_college=True<br>residence=%{x}<br>count=%{y}<extra></extra>",
         "legendgroup": "True",
         "marker": {
          "color": "#636efa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "True",
         "offsetgroup": "True",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "Urban",
          "Urban",
          "Urban",
          "Rural",
          "Rural",
          "Rural",
          "Rural",
          "Urban",
          "Urban",
          "Urban",
          "Urban",
          "Urban",
          "Rural",
          "Rural",
          "Urban",
          "Urban",
          "Urban",
          "Urban",
          "Rural",
          "Urban",
          "Urban",
          "Rural",
          "Rural",
          "Urban",
          "Rural",
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          "Urban",
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          "Urban",
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          "Urban",
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    "fig = px.bar(college, x='residence', color = 'in_college', barmode='group',)\n",
    "fig.show()"
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  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "39b086d0",
   "metadata": {},
   "outputs": [
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   "source": [
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    "fig = px.bar(college, x='interest', color = 'in_college', barmode='group',)\n",
    "fig.show()"
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  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d40b1811",
   "metadata": {},
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    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "33343841",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='average_grades', ylabel='Density'>"
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     },
     "execution_count": 13,
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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "sns.kdeplot(x=\"average_grades\", hue=\"in_college\", data=college)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5710221",
   "metadata": {},
   "source": [
    "## ML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "75062b7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "Name: in_college, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_cat = college.in_college\n",
    "y = y_cat.map({False: 0, True: 1})\n",
    "y.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "30c60552",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type_school</th>\n",
       "      <th>school_accreditation</th>\n",
       "      <th>gender</th>\n",
       "      <th>interest</th>\n",
       "      <th>residence</th>\n",
       "      <th>parent_age</th>\n",
       "      <th>parent_salary</th>\n",
       "      <th>house_area</th>\n",
       "      <th>average_grades</th>\n",
       "      <th>parent_was_in_college</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Academic</td>\n",
       "      <td>A</td>\n",
       "      <td>Male</td>\n",
       "      <td>Less Interested</td>\n",
       "      <td>Urban</td>\n",
       "      <td>56</td>\n",
       "      <td>6950000</td>\n",
       "      <td>83.0</td>\n",
       "      <td>84.09</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Academic</td>\n",
       "      <td>A</td>\n",
       "      <td>Male</td>\n",
       "      <td>Less Interested</td>\n",
       "      <td>Urban</td>\n",
       "      <td>57</td>\n",
       "      <td>4410000</td>\n",
       "      <td>76.8</td>\n",
       "      <td>86.91</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Academic</td>\n",
       "      <td>B</td>\n",
       "      <td>Female</td>\n",
       "      <td>Very Interested</td>\n",
       "      <td>Urban</td>\n",
       "      <td>50</td>\n",
       "      <td>6500000</td>\n",
       "      <td>80.6</td>\n",
       "      <td>87.43</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  type_school school_accreditation  gender         interest residence  \\\n",
       "0    Academic                    A    Male  Less Interested     Urban   \n",
       "1    Academic                    A    Male  Less Interested     Urban   \n",
       "2    Academic                    B  Female  Very Interested     Urban   \n",
       "\n",
       "   parent_age  parent_salary  house_area  average_grades  \\\n",
       "0          56        6950000        83.0           84.09   \n",
       "1          57        4410000        76.8           86.91   \n",
       "2          50        6500000        80.6           87.43   \n",
       "\n",
       "   parent_was_in_college  \n",
       "0                  False  \n",
       "1                  False  \n",
       "2                  False  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = college.drop(['in_college'],axis = 1 )\n",
    "\n",
    "X.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22faabae",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a3c81f0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1a642c32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['type_school', 'school_accreditation', 'gender', 'interest',\n",
       "       'residence', 'parent_age', 'parent_salary', 'house_area',\n",
       "       'average_grades', 'parent_was_in_college'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_columns = X_train.columns\n",
    "X_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e593d61f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "column_trans = make_column_transformer((OneHotEncoder(), [0, 1, 2,3,4,9]), remainder='passthrough')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3716df61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "800"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5e014b11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.000e+00, 0.000e+00, 0.000e+00, 1.000e+00, 1.000e+00, 0.000e+00,\n",
       "        0.000e+00, 0.000e+00, 0.000e+00, 1.000e+00, 0.000e+00, 1.000e+00,\n",
       "        0.000e+00, 0.000e+00, 1.000e+00, 4.800e+01, 6.750e+06, 9.100e+01,\n",
       "        8.501e+01]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = column_trans.fit_transform(X_train)\n",
    "X_test = column_trans.transform(X_test)\n",
    "X_test[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "00430362",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "679f28ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.82289054, -0.82289054, -0.96801137, ...,  0.23224301,\n",
       "        -0.0425159 , -0.4102218 ],\n",
       "       [-1.21522845,  1.21522845, -0.96801137, ..., -0.89704925,\n",
       "        -1.95086304, -1.03276017],\n",
       "       [ 0.82289054, -0.82289054, -0.96801137, ..., -1.34728996,\n",
       "         0.06133973,  1.54503218],\n",
       "       ...,\n",
       "       [-1.21522845,  1.21522845, -0.96801137, ..., -0.91919224,\n",
       "        -1.81455253, -1.24732436],\n",
       "       [ 0.82289054, -0.82289054, -0.96801137, ..., -1.19228906,\n",
       "        -0.35408278,  0.93458194],\n",
       "       [-1.21522845,  1.21522845,  1.03304572, ...,  2.19558774,\n",
       "        -0.56828501, -0.75473333]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "4888134c",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Input X must be non-negative.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\JOONPA~1\\AppData\\Local\\Temp/ipykernel_140648/2437001243.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mselector\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSelectKBest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchi2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Weighting method is chi-squared\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mXi\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mselector\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mXi\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36mfit_transform\u001b[1;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[0;32m    700\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    701\u001b[0m             \u001b[1;31m# fit method of arity 2 (supervised transformation)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 702\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    703\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    704\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\feature_selection\\_univariate_selection.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m    351\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    352\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_params\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 353\u001b[1;33m         \u001b[0mscore_func_ret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscore_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    354\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscore_func_ret\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    355\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscores_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpvalues_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscore_func_ret\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\feature_selection\\_univariate_selection.py\u001b[0m in \u001b[0;36mchi2\u001b[1;34m(X, y)\u001b[0m\n\u001b[0;32m    216\u001b[0m     \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'csr'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    217\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0missparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 218\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Input X must be non-negative.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    219\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    220\u001b[0m     \u001b[0mY\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLabelBinarizer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Input X must be non-negative."
     ]
    }
   ],
   "source": [
    "selector = SelectKBest(chi2, k = 3) # Weighting method is chi-squared\n",
    "Xi = selector.fit_transform(X_train,y_train)\n",
    "Xi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "57b7b5f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import LeaveOneOut"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 24,
=======
   "execution_count": 62,
>>>>>>> e1d1184ffa289bd276b5bba148c1705384e3ee50
   "id": "b23caf23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
<<<<<<< HEAD
      "Fitting 800 folds for each of 1 candidates, totalling 800 fits\n",
      "[CV 1/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   1.7s\n",
      "[CV 2/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.2s\n",
      "[CV 3/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.2s\n",
      "[CV 4/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=1.000) total time=   0.2s\n",
      "[CV 5/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.2s\n",
      "[CV 6/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 7/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 8/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.2s\n",
      "[CV 9/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.2s\n",
      "[CV 10/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 11/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 12/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=0.000) total time=   0.1s\n",
      "[CV 13/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 14/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 15/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 16/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=0.000) total time=   0.2s\n",
      "[CV 17/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 18/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 19/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 20/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 21/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=1.000) total time=   0.1s\n",
      "[CV 22/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 23/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 24/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 25/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 26/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.2s\n",
      "[CV 27/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 28/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.2s\n",
      "[CV 29/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=0.000) total time=   0.1s\n",
      "[CV 30/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 31/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=0.000) total time=   0.1s\n",
      "[CV 32/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 33/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=0.000) total time=   0.2s\n",
      "[CV 34/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.2s\n",
      "[CV 35/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.2s\n",
      "[CV 36/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=0.000) total time=   0.1s\n",
      "[CV 37/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 38/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 39/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=1.000) total time=   0.2s\n",
      "[CV 40/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 41/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=1.000) total time=   0.2s\n",
      "[CV 42/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=1.000) total time=   0.1s\n",
      "[CV 43/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.935, test=1.000) total time=   0.2s\n",
      "[CV 44/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=1.000) total time=   0.1s\n",
      "[CV 45/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.934, test=1.000) total time=   0.1s\n",
      "[CV 46/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.2s\n",
      "[CV 47/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=0.000) total time=   0.2s\n",
      "[CV 48/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 49/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=1.000) total time=   0.1s\n",
      "[CV 50/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=1.000) total time=   0.1s\n",
      "[CV 51/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=0.000) total time=   0.1s\n",
      "[CV 52/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 53/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 54/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 55/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 56/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.934, test=0.000) total time=   0.1s\n",
      "[CV 57/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 58/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 59/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 60/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 61/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.2s\n",
      "[CV 62/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 63/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 64/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 65/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 66/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.2s\n",
      "[CV 67/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=0.000) total time=   0.2s\n",
      "[CV 68/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=0.000) total time=   0.1s\n",
      "[CV 69/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 70/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 71/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=0.000) total time=   0.1s\n",
      "[CV 72/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=0.000) total time=   0.1s\n",
      "[CV 73/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 74/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 75/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 76/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 77/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 78/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=0.000) total time=   0.1s\n",
      "[CV 79/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=0.000) total time=   0.1s\n",
      "[CV 80/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 81/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.2s\n",
      "[CV 82/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 83/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 84/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=0.000) total time=   0.1s\n",
      "[CV 85/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 86/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 87/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.2s\n",
      "[CV 88/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=0.000) total time=   0.2s\n",
      "[CV 89/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.921, test=1.000) total time=   0.2s\n",
      "[CV 90/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=1.000) total time=   0.2s\n",
      "[CV 91/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=1.000) total time=   0.1s\n",
      "[CV 92/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.921, test=1.000) total time=   0.1s\n",
      "[CV 93/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.2s\n",
      "[CV 94/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.920, test=1.000) total time=   0.1s\n",
      "[CV 95/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=1.000) total time=   0.1s\n",
      "[CV 96/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=1.000) total time=   0.2s\n",
      "[CV 97/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=1.000) total time=   0.1s\n",
      "[CV 98/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 99/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 100/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.921, test=1.000) total time=   0.2s\n",
      "[CV 101/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=1.000) total time=   0.2s\n",
      "[CV 102/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 103/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 104/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 105/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=1.000) total time=   0.1s\n",
      "[CV 106/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=1.000) total time=   0.1s\n",
      "[CV 107/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.921, test=0.000) total time=   0.2s\n",
      "[CV 108/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.2s\n",
      "[CV 109/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 110/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=1.000) total time=   0.1s\n",
      "[CV 111/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 112/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.2s\n",
      "[CV 113/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=0.000) total time=   0.1s\n",
      "[CV 114/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 115/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 116/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 117/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 118/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 119/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.2s\n",
      "[CV 120/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 121/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.2s\n",
      "[CV 122/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 123/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 124/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.2s\n",
      "[CV 125/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 126/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.920, test=1.000) total time=   0.1s\n",
      "[CV 127/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 128/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 129/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=0.000) total time=   0.2s\n",
      "[CV 130/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 131/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.2s\n",
      "[CV 132/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.2s\n",
      "[CV 133/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.2s\n",
      "[CV 134/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 135/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=0.000) total time=   0.1s\n",
      "[CV 136/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 137/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=0.000) total time=   0.1s\n",
      "[CV 138/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 139/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 140/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=0.000) total time=   0.2s\n",
      "[CV 141/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 142/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 143/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.2s\n",
      "[CV 144/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 145/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 146/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.2s\n",
      "[CV 147/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.2s\n",
      "[CV 148/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.2s\n",
      "[CV 149/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 150/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=0.000) total time=   0.2s\n",
      "[CV 151/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 152/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.2s\n",
      "[CV 153/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.2s\n",
      "[CV 154/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 155/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 156/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 157/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 158/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 159/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 160/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=1.000) total time=   0.1s\n",
      "[CV 161/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.934, test=1.000) total time=   0.1s\n",
      "[CV 162/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.2s\n",
      "[CV 163/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 164/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 165/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 166/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 167/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=0.000) total time=   0.1s\n",
      "[CV 168/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 169/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 170/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=1.000) total time=   0.1s\n",
      "[CV 171/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 172/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.2s\n",
      "[CV 173/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=0.000) total time=   0.2s\n",
      "[CV 174/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.2s\n",
      "[CV 175/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=0.000) total time=   0.2s\n",
      "[CV 176/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.2s\n",
      "[CV 177/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=0.000) total time=   0.2s\n",
      "[CV 178/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.2s\n",
      "[CV 179/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=1.000) total time=   0.2s\n",
      "[CV 180/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=1.000) total time=   0.2s\n",
      "[CV 181/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=0.000) total time=   0.2s\n",
      "[CV 182/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.2s\n",
      "[CV 183/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.2s\n",
      "[CV 184/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.2s\n",
      "[CV 185/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.2s\n",
      "[CV 186/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.2s\n",
      "[CV 187/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=1.000) total time=   0.2s\n",
      "[CV 188/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=1.000) total time=   0.2s\n",
      "[CV 189/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.2s\n",
      "[CV 190/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.2s\n",
      "[CV 191/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.2s\n",
      "[CV 192/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=0.000) total time=   0.2s\n",
      "[CV 193/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.2s\n",
      "[CV 194/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.2s\n",
      "[CV 195/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.2s\n",
      "[CV 196/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=0.000) total time=   0.2s\n",
      "[CV 197/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.2s\n",
      "[CV 198/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.2s\n",
      "[CV 199/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=0.000) total time=   0.2s\n",
      "[CV 200/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.2s\n",
      "[CV 201/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=1.000) total time=   0.2s\n",
      "[CV 202/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.934, test=1.000) total time=   0.2s\n",
      "[CV 203/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.935, test=1.000) total time=   0.2s\n",
      "[CV 204/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=0.000) total time=   0.1s\n",
      "[CV 205/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.935, test=1.000) total time=   0.2s\n",
      "[CV 206/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.934, test=1.000) total time=   0.1s\n",
      "[CV 207/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.934, test=1.000) total time=   0.1s\n",
      "[CV 208/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.935, test=1.000) total time=   0.1s\n",
      "[CV 209/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.936, test=1.000) total time=   0.1s\n",
      "[CV 210/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.934, test=1.000) total time=   0.2s\n",
      "[CV 211/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.937, test=1.000) total time=   0.2s\n",
      "[CV 212/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.936, test=1.000) total time=   0.2s\n",
      "[CV 213/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.937, test=0.000) total time=   0.2s\n",
      "[CV 214/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.935, test=1.000) total time=   0.2s\n",
      "[CV 215/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=1.000) total time=   0.1s\n",
      "[CV 216/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 217/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=1.000) total time=   0.1s\n",
      "[CV 218/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.934, test=1.000) total time=   0.1s\n",
      "[CV 219/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.2s\n",
      "[CV 220/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=1.000) total time=   0.1s\n",
      "[CV 221/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 222/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 223/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.2s\n",
      "[CV 224/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.2s\n",
      "[CV 225/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 226/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.2s\n",
      "[CV 227/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=0.000) total time=   0.2s\n",
      "[CV 228/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.2s\n",
      "[CV 229/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.2s\n",
      "[CV 230/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.2s\n",
      "[CV 231/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.2s\n",
      "[CV 232/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.2s\n",
      "[CV 233/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 234/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 235/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 236/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 237/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.2s\n",
      "[CV 238/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=1.000) total time=   0.1s\n",
      "[CV 239/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.2s\n",
      "[CV 240/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 241/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=1.000) total time=   0.1s\n",
      "[CV 242/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.921, test=0.000) total time=   0.1s\n",
      "[CV 243/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.921, test=1.000) total time=   0.1s\n",
      "[CV 244/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 245/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=0.000) total time=   0.1s\n",
      "[CV 246/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 247/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 248/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 249/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 250/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 251/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=1.000) total time=   0.1s\n",
      "[CV 252/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 253/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 254/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=0.000) total time=   0.1s\n",
      "[CV 255/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 256/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.2s\n",
      "[CV 257/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=0.000) total time=   0.1s\n",
      "[CV 258/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=0.000) total time=   0.1s\n",
      "[CV 259/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 260/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 261/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 262/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 263/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 264/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 265/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.934, test=1.000) total time=   0.1s\n",
      "[CV 266/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 267/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 268/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 269/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.921, test=1.000) total time=   0.1s\n",
      "[CV 270/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 271/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 272/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.2s\n",
      "[CV 273/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=1.000) total time=   0.1s\n",
      "[CV 274/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 275/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.2s\n",
      "[CV 276/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 277/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 278/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 279/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=0.000) total time=   0.1s\n",
      "[CV 280/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.2s\n",
      "[CV 281/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=1.000) total time=   0.1s\n",
      "[CV 282/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=1.000) total time=   0.1s\n",
      "[CV 283/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.921, test=1.000) total time=   0.2s\n",
      "[CV 284/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=0.000) total time=   0.1s\n",
      "[CV 285/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 286/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.2s\n",
      "[CV 287/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 288/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 289/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.2s\n",
      "[CV 290/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 291/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 292/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 293/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.2s\n",
      "[CV 294/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.2s\n",
      "[CV 295/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.921, test=0.000) total time=   0.2s\n",
      "[CV 296/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=0.000) total time=   0.2s\n",
      "[CV 297/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 298/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 299/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 300/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 301/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 302/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 303/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 304/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.917, test=1.000) total time=   0.1s\n",
      "[CV 305/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.920, test=1.000) total time=   0.1s\n",
      "[CV 306/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 307/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.921, test=1.000) total time=   0.1s\n",
      "[CV 308/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=1.000) total time=   0.1s\n",
      "[CV 309/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 310/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.921, test=1.000) total time=   0.2s\n",
      "[CV 311/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 312/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 313/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 314/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 315/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 316/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 317/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 318/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 319/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 320/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=0.000) total time=   0.1s\n",
      "[CV 321/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 322/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 323/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=1.000) total time=   0.1s\n",
      "[CV 324/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 325/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.921, test=1.000) total time=   0.1s\n",
      "[CV 326/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 327/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=1.000) total time=   0.1s\n",
      "[CV 328/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 329/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 330/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 331/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 332/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 333/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 334/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 335/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=1.000) total time=   0.1s\n",
      "[CV 336/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 337/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.920, test=1.000) total time=   0.1s\n",
      "[CV 338/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.921, test=0.000) total time=   0.1s\n",
      "[CV 339/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 340/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 341/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=0.000) total time=   0.1s\n",
      "[CV 342/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 343/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=0.000) total time=   0.1s\n",
      "[CV 344/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 345/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.2s\n",
      "[CV 346/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.2s\n",
      "[CV 347/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 348/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=0.000) total time=   0.1s\n",
      "[CV 349/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.2s\n",
      "[CV 350/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.921, test=1.000) total time=   0.2s\n",
      "[CV 351/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.2s\n",
      "[CV 352/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.2s\n",
      "[CV 353/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=1.000) total time=   0.2s\n",
      "[CV 354/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.2s\n",
      "[CV 355/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 356/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 357/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 358/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 359/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 360/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=1.000) total time=   0.1s\n",
      "[CV 361/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 362/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 363/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 364/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=0.000) total time=   0.1s\n",
      "[CV 365/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 366/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=1.000) total time=   0.1s\n",
      "[CV 367/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 368/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=0.000) total time=   0.1s\n",
      "[CV 369/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 370/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=0.000) total time=   0.2s\n",
      "[CV 371/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.2s\n",
      "[CV 372/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.2s\n",
      "[CV 373/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=1.000) total time=   0.1s\n",
      "[CV 374/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 375/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 376/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 377/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 378/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=1.000) total time=   0.2s\n",
      "[CV 379/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 380/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=1.000) total time=   0.2s\n",
      "[CV 381/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 382/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 383/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.2s\n",
      "[CV 384/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 385/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 386/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.921, test=1.000) total time=   0.2s\n",
      "[CV 387/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 388/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 389/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 390/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 391/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 392/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 393/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 394/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 395/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 396/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 397/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 398/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=1.000) total time=   0.1s\n",
      "[CV 399/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 400/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 401/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=0.000) total time=   0.1s\n",
      "[CV 402/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.2s\n",
      "[CV 403/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 404/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 405/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 406/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 407/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=0.000) total time=   0.1s\n",
      "[CV 408/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 409/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 410/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 411/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 412/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 413/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=0.000) total time=   0.1s\n",
      "[CV 414/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 415/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 416/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.2s\n",
      "[CV 417/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 418/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 419/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 420/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 421/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 422/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 423/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 424/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 425/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.2s\n",
      "[CV 426/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 427/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 428/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=0.000) total time=   0.1s\n",
      "[CV 429/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 430/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 431/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 432/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 433/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 434/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=0.000) total time=   0.1s\n",
      "[CV 435/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 436/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 437/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 438/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 439/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 440/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 441/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.934, test=1.000) total time=   0.1s\n",
      "[CV 442/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=1.000) total time=   0.1s\n",
      "[CV 443/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=1.000) total time=   0.1s\n",
      "[CV 444/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=1.000) total time=   0.1s\n",
      "[CV 445/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 446/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 447/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 448/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 449/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 450/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=0.000) total time=   0.2s\n",
      "[CV 451/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=0.000) total time=   0.1s\n",
      "[CV 452/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 453/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 454/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 455/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 456/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 457/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 458/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 459/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.934, test=1.000) total time=   0.1s\n",
      "[CV 460/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=0.000) total time=   0.1s\n",
      "[CV 461/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 462/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=0.000) total time=   0.1s\n",
      "[CV 463/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 464/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.2s\n",
      "[CV 465/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 466/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 467/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 468/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 469/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.2s\n",
      "[CV 470/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=0.000) total time=   0.1s\n",
      "[CV 471/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 472/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 473/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 474/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 475/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 476/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 477/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=0.000) total time=   0.1s\n",
      "[CV 478/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 479/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 480/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.2s\n",
      "[CV 481/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.2s\n",
      "[CV 482/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.2s\n",
      "[CV 483/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 484/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 485/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.2s\n",
      "[CV 486/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 487/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.2s\n",
      "[CV 488/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 489/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.934, test=1.000) total time=   0.1s\n",
      "[CV 490/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 491/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 492/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 493/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=0.000) total time=   0.1s\n",
      "[CV 494/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 495/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 496/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 497/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 498/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 499/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 500/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 501/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 502/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 503/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.2s\n",
      "[CV 504/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 505/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 506/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 507/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 508/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 509/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.2s\n",
      "[CV 510/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 511/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 512/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 513/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.2s\n",
      "[CV 514/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.2s\n",
      "[CV 515/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 516/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 517/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 518/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 519/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 520/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=1.000) total time=   0.1s\n",
      "[CV 521/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 522/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=0.000) total time=   0.1s\n",
      "[CV 523/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 524/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=0.000) total time=   0.1s\n",
      "[CV 525/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 526/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 527/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=0.000) total time=   0.1s\n",
      "[CV 528/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.2s\n",
      "[CV 529/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=0.000) total time=   0.2s\n",
      "[CV 530/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.2s\n",
      "[CV 531/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=0.000) total time=   0.2s\n",
      "[CV 532/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 533/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=1.000) total time=   0.1s\n",
      "[CV 534/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 535/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 536/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 537/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.934, test=1.000) total time=   0.1s\n",
      "[CV 538/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 539/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 540/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.2s\n",
      "[CV 541/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.2s\n",
      "[CV 542/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 543/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=0.000) total time=   0.2s\n",
      "[CV 544/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 545/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 546/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 547/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 548/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=0.000) total time=   0.1s\n",
      "[CV 549/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 550/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 551/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 552/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 553/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=0.000) total time=   0.1s\n",
      "[CV 554/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 555/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 556/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 557/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=0.000) total time=   0.1s\n",
      "[CV 558/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 559/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 560/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.2s\n",
      "[CV 561/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 562/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 563/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 564/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.2s\n",
      "[CV 565/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.2s\n",
      "[CV 566/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.2s\n",
      "[CV 567/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=1.000) total time=   0.1s\n",
      "[CV 568/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.2s\n",
      "[CV 569/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 570/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=0.000) total time=   0.2s\n",
      "[CV 571/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 572/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 573/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.2s\n",
      "[CV 574/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.2s\n",
      "[CV 575/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 576/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 577/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 578/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 579/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.2s\n",
      "[CV 580/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=0.000) total time=   0.1s\n",
      "[CV 581/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=1.000) total time=   0.1s\n",
      "[CV 582/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 583/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=0.000) total time=   0.1s\n",
      "[CV 584/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 585/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 586/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 587/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 588/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.934, test=1.000) total time=   0.1s\n",
      "[CV 589/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.935, test=0.000) total time=   0.1s\n",
      "[CV 590/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.934, test=0.000) total time=   0.1s\n",
      "[CV 591/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=1.000) total time=   0.1s\n",
      "[CV 592/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=0.000) total time=   0.1s\n",
      "[CV 593/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 594/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 595/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 596/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 597/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 598/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 599/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.2s\n",
      "[CV 600/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 601/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=0.000) total time=   0.1s\n",
      "[CV 602/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=0.000) total time=   0.1s\n",
      "[CV 603/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 604/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 605/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.2s\n",
      "[CV 606/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 607/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=1.000) total time=   0.1s\n",
      "[CV 608/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=0.000) total time=   0.1s\n",
      "[CV 609/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 610/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=1.000) total time=   0.1s\n",
      "[CV 611/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 612/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.934, test=1.000) total time=   0.1s\n",
      "[CV 613/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=1.000) total time=   0.1s\n",
      "[CV 614/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.934, test=1.000) total time=   0.2s\n",
      "[CV 615/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 616/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 617/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=1.000) total time=   0.2s\n",
      "[CV 618/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.2s\n",
      "[CV 619/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 620/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=1.000) total time=   0.1s\n",
      "[CV 621/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 622/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.2s\n",
      "[CV 623/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.934, test=0.000) total time=   0.1s\n",
      "[CV 624/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 625/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 626/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.2s\n",
      "[CV 627/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 628/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 629/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 630/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.2s\n",
      "[CV 631/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 632/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 633/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 634/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 635/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 636/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.2s\n",
      "[CV 637/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.2s\n",
      "[CV 638/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.2s\n",
      "[CV 639/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.2s\n",
      "[CV 640/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 641/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 642/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 643/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 644/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 645/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 646/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=0.000) total time=   0.2s\n",
      "[CV 647/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.2s\n",
      "[CV 648/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=0.000) total time=   0.1s\n",
      "[CV 649/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 650/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 651/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 652/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.2s\n",
      "[CV 653/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 654/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 655/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.2s\n",
      "[CV 656/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 657/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 658/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.934, test=1.000) total time=   0.1s\n",
      "[CV 659/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 660/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 661/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 662/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.2s\n",
      "[CV 663/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 664/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 665/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 666/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 667/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 668/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 669/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=0.000) total time=   0.1s\n",
      "[CV 670/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 671/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 672/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=1.000) total time=   0.1s\n",
      "[CV 673/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 674/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 675/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.2s\n",
      "[CV 676/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=0.000) total time=   0.1s\n",
      "[CV 677/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.2s\n",
      "[CV 678/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.934, test=1.000) total time=   0.2s\n",
      "[CV 679/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.2s\n",
      "[CV 680/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.932, test=1.000) total time=   0.1s\n",
      "[CV 681/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.2s\n",
      "[CV 682/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.4s\n",
      "[CV 683/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.2s\n",
      "[CV 684/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.2s\n",
      "[CV 685/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.3s\n",
      "[CV 686/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.2s\n",
      "[CV 687/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.934, test=1.000) total time=   0.2s\n",
      "[CV 688/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 689/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 690/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=0.000) total time=   0.1s\n",
      "[CV 691/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=0.000) total time=   0.2s\n",
      "[CV 692/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.3s\n",
      "[CV 693/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.2s\n",
      "[CV 694/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.3s\n",
      "[CV 695/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.2s\n",
      "[CV 696/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 697/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 698/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=1.000) total time=   0.2s\n",
      "[CV 699/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.2s\n",
      "[CV 700/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=0.000) total time=   0.2s\n",
      "[CV 701/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.2s\n",
      "[CV 702/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.3s\n",
      "[CV 703/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.2s\n",
      "[CV 704/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 705/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 706/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.2s\n",
      "[CV 707/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.2s\n",
      "[CV 708/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.3s\n",
      "[CV 709/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.2s\n",
      "[CV 710/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.2s\n",
      "[CV 711/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.2s\n",
      "[CV 712/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.3s\n",
      "[CV 713/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.2s\n",
      "[CV 714/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.2s\n",
      "[CV 715/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.2s\n",
      "[CV 716/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.2s\n",
      "[CV 717/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.2s\n",
      "[CV 718/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.2s\n",
      "[CV 719/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 720/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.2s\n",
      "[CV 721/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.2s\n",
      "[CV 722/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.2s\n",
      "[CV 723/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 724/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 725/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.2s\n",
      "[CV 726/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.2s\n",
      "[CV 727/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.2s\n",
      "[CV 728/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=0.000) total time=   0.1s\n",
      "[CV 729/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.2s\n",
      "[CV 730/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.921, test=0.000) total time=   0.2s\n",
      "[CV 731/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.2s\n",
      "[CV 732/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 733/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 734/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.2s\n",
      "[CV 735/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.2s\n",
      "[CV 736/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.2s\n",
      "[CV 737/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 738/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.2s\n",
      "[CV 739/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.2s\n",
      "[CV 740/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.2s\n",
      "[CV 741/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 742/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=0.000) total time=   0.1s\n",
      "[CV 743/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.2s\n",
      "[CV 744/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 745/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 746/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 747/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.1s\n",
      "[CV 748/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.2s\n",
      "[CV 749/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 750/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 751/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=0.000) total time=   0.1s\n",
      "[CV 752/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 753/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=0.000) total time=   0.2s\n",
      "[CV 754/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 755/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 756/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.921, test=1.000) total time=   0.2s\n",
      "[CV 757/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 758/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 759/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 760/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.2s\n",
      "[CV 761/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=1.000) total time=   0.2s\n",
      "[CV 762/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.2s\n",
      "[CV 763/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 764/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.2s\n",
      "[CV 765/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.2s\n",
      "[CV 766/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 767/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 768/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.1s\n",
      "[CV 769/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 770/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=1.000) total time=   0.1s\n",
      "[CV 771/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=0.000) total time=   0.1s\n",
      "[CV 772/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 773/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=1.000) total time=   0.2s\n",
      "[CV 774/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=1.000) total time=   0.1s\n",
      "[CV 775/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=0.000) total time=   0.1s\n",
      "[CV 776/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 777/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 778/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.922, test=1.000) total time=   0.1s\n",
      "[CV 779/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 780/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 781/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 782/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 783/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 784/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=0.000) total time=   0.1s\n",
      "[CV 785/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=1.000) total time=   0.1s\n",
      "[CV 786/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 787/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.931, test=0.000) total time=   0.2s\n",
      "[CV 788/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=1.000) total time=   0.1s\n",
      "[CV 789/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 790/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.2s\n",
      "[CV 791/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=0.000) total time=   0.2s\n",
      "[CV 792/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.930, test=1.000) total time=   0.2s\n",
      "[CV 793/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=0.000) total time=   0.1s\n",
      "[CV 794/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n",
      "[CV 795/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=0.000) total time=   0.2s\n",
      "[CV 796/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.924, test=1.000) total time=   0.1s\n",
      "[CV 797/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.925, test=1.000) total time=   0.2s\n",
      "[CV 798/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.929, test=1.000) total time=   0.1s\n",
      "[CV 799/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.926, test=0.000) total time=   0.2s\n",
      "[CV 800/800] END fselection__k=13, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.927, test=1.000) total time=   0.1s\n"
=======
      "Fitting 5 folds for each of 100 candidates, totalling 500 fits\n",
      "[CV 1/5] END fselection__k=4, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.909, test=0.838) total time=   0.2s\n",
      "[CV 2/5] END fselection__k=4, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.894, test=0.844) total time=   0.3s\n",
      "[CV 3/5] END fselection__k=4, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.886, test=0.844) total time=   0.2s\n",
      "[CV 4/5] END fselection__k=4, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.895, test=0.812) total time=   0.1s\n",
      "[CV 5/5] END fselection__k=4, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.898, test=0.812) total time=   0.1s\n",
      "[CV 1/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=9, random_forest__min_samples_leaf=1, random_forest__min_samples_split=4;, score=(train=0.975, test=0.844) total time=   0.1s\n",
      "[CV 2/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=9, random_forest__min_samples_leaf=1, random_forest__min_samples_split=4;, score=(train=0.961, test=0.831) total time=   0.3s\n",
      "[CV 3/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=9, random_forest__min_samples_leaf=1, random_forest__min_samples_split=4;, score=(train=0.956, test=0.863) total time=   0.2s\n",
      "[CV 4/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=9, random_forest__min_samples_leaf=1, random_forest__min_samples_split=4;, score=(train=0.958, test=0.819) total time=   0.3s\n",
      "[CV 5/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=9, random_forest__min_samples_leaf=1, random_forest__min_samples_split=4;, score=(train=0.961, test=0.812) total time=   0.3s\n",
      "[CV 1/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=4, random_forest__min_samples_split=8;, score=(train=0.823, test=0.731) total time=   1.2s\n",
      "[CV 2/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=4, random_forest__min_samples_split=8;, score=(train=0.806, test=0.825) total time=   1.5s\n",
      "[CV 3/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=4, random_forest__min_samples_split=8;, score=(train=0.792, test=0.787) total time=   1.4s\n",
      "[CV 4/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=4, random_forest__min_samples_split=8;, score=(train=0.820, test=0.781) total time=   1.1s\n",
      "[CV 5/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=4, random_forest__min_samples_split=8;, score=(train=0.820, test=0.750) total time=   1.5s\n",
      "[CV 1/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=8, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.920, test=0.800) total time=   1.0s\n",
      "[CV 2/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=8, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.936, test=0.812) total time=   1.0s\n",
      "[CV 3/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=8, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.922, test=0.863) total time=   1.0s\n",
      "[CV 4/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=8, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.942, test=0.819) total time=   1.5s\n",
      "[CV 5/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=8, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.922, test=0.806) total time=   1.4s\n",
      "[CV 1/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=8;, score=(train=0.822, test=0.731) total time=   1.2s\n",
      "[CV 2/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=8;, score=(train=0.806, test=0.825) total time=   1.1s\n",
      "[CV 3/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=8;, score=(train=0.794, test=0.787) total time=   1.0s\n",
      "[CV 4/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=8;, score=(train=0.823, test=0.781) total time=   1.0s\n",
      "[CV 5/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=8;, score=(train=0.827, test=0.744) total time=   1.0s\n",
      "[CV 1/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=9, random_forest__min_samples_leaf=3, random_forest__min_samples_split=2;, score=(train=0.961, test=0.856) total time=   0.2s\n",
      "[CV 2/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=9, random_forest__min_samples_leaf=3, random_forest__min_samples_split=2;, score=(train=0.948, test=0.850) total time=   0.2s\n",
      "[CV 3/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=9, random_forest__min_samples_leaf=3, random_forest__min_samples_split=2;, score=(train=0.947, test=0.863) total time=   0.2s\n",
      "[CV 4/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=9, random_forest__min_samples_leaf=3, random_forest__min_samples_split=2;, score=(train=0.942, test=0.819) total time=   0.2s\n",
      "[CV 5/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=9, random_forest__min_samples_leaf=3, random_forest__min_samples_split=2;, score=(train=0.936, test=0.838) total time=   0.2s\n",
      "[CV 1/5] END fselection__k=10, poly_features__degree=2, random_forest__max_depth=4, random_forest__min_samples_leaf=3, random_forest__min_samples_split=6;, score=(train=0.872, test=0.831) total time=   1.0s\n",
      "[CV 2/5] END fselection__k=10, poly_features__degree=2, random_forest__max_depth=4, random_forest__min_samples_leaf=3, random_forest__min_samples_split=6;, score=(train=0.867, test=0.838) total time=   1.3s\n",
      "[CV 3/5] END fselection__k=10, poly_features__degree=2, random_forest__max_depth=4, random_forest__min_samples_leaf=3, random_forest__min_samples_split=6;, score=(train=0.863, test=0.850) total time=   1.1s\n",
      "[CV 4/5] END fselection__k=10, poly_features__degree=2, random_forest__max_depth=4, random_forest__min_samples_leaf=3, random_forest__min_samples_split=6;, score=(train=0.875, test=0.794) total time=   1.0s\n",
      "[CV 5/5] END fselection__k=10, poly_features__degree=2, random_forest__max_depth=4, random_forest__min_samples_leaf=3, random_forest__min_samples_split=6;, score=(train=0.872, test=0.812) total time=   1.0s\n",
      "[CV 1/5] END fselection__k=4, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=4, random_forest__min_samples_split=4;, score=(train=0.850, test=0.725) total time=   1.0s\n",
      "[CV 2/5] END fselection__k=4, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=4, random_forest__min_samples_split=4;, score=(train=0.819, test=0.819) total time=   1.2s\n",
      "[CV 3/5] END fselection__k=4, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=4, random_forest__min_samples_split=4;, score=(train=0.848, test=0.831) total time=   1.5s\n",
      "[CV 4/5] END fselection__k=4, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=4, random_forest__min_samples_split=4;, score=(train=0.848, test=0.775) total time=   2.1s\n",
      "[CV 5/5] END fselection__k=4, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=4, random_forest__min_samples_split=4;, score=(train=0.838, test=0.775) total time=   2.1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/5] END fselection__k=8, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=2, random_forest__min_samples_split=5;, score=(train=0.958, test=0.844) total time=   0.7s\n",
      "[CV 2/5] END fselection__k=8, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=2, random_forest__min_samples_split=5;, score=(train=0.928, test=0.863) total time=   1.4s\n",
      "[CV 3/5] END fselection__k=8, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=2, random_forest__min_samples_split=5;, score=(train=0.947, test=0.894) total time=   1.5s\n",
      "[CV 4/5] END fselection__k=8, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=2, random_forest__min_samples_split=5;, score=(train=0.944, test=0.881) total time=   1.0s\n",
      "[CV 5/5] END fselection__k=8, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=2, random_forest__min_samples_split=5;, score=(train=0.958, test=0.887) total time=   0.8s\n",
      "[CV 1/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.891, test=0.806) total time=   5.1s\n",
      "[CV 2/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.902, test=0.819) total time=   2.6s\n",
      "[CV 3/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.883, test=0.850) total time=   2.0s\n",
      "[CV 4/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.894, test=0.800) total time=   1.9s\n",
      "[CV 5/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.881, test=0.819) total time=   2.5s\n",
      "[CV 1/5] END fselection__k=4, poly_features__degree=1, random_forest__max_depth=4, random_forest__min_samples_leaf=4, random_forest__min_samples_split=3;, score=(train=0.886, test=0.838) total time=   0.4s\n",
      "[CV 2/5] END fselection__k=4, poly_features__degree=1, random_forest__max_depth=4, random_forest__min_samples_leaf=4, random_forest__min_samples_split=3;, score=(train=0.856, test=0.850) total time=   0.4s\n",
      "[CV 3/5] END fselection__k=4, poly_features__degree=1, random_forest__max_depth=4, random_forest__min_samples_leaf=4, random_forest__min_samples_split=3;, score=(train=0.850, test=0.844) total time=   0.4s\n",
      "[CV 4/5] END fselection__k=4, poly_features__degree=1, random_forest__max_depth=4, random_forest__min_samples_leaf=4, random_forest__min_samples_split=3;, score=(train=0.867, test=0.787) total time=   0.4s\n",
      "[CV 5/5] END fselection__k=4, poly_features__degree=1, random_forest__max_depth=4, random_forest__min_samples_leaf=4, random_forest__min_samples_split=3;, score=(train=0.869, test=0.844) total time=   0.4s\n",
      "[CV 1/5] END fselection__k=1, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=3, random_forest__min_samples_split=6;, score=(train=0.802, test=0.775) total time=   0.4s\n",
      "[CV 2/5] END fselection__k=1, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=3, random_forest__min_samples_split=6;, score=(train=0.783, test=0.812) total time=   0.5s\n",
      "[CV 3/5] END fselection__k=1, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=3, random_forest__min_samples_split=6;, score=(train=0.787, test=0.787) total time=   0.5s\n",
      "[CV 4/5] END fselection__k=1, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=3, random_forest__min_samples_split=6;, score=(train=0.795, test=0.756) total time=   0.4s\n",
      "[CV 5/5] END fselection__k=1, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=3, random_forest__min_samples_split=6;, score=(train=0.794, test=0.756) total time=   0.5s\n",
      "[CV 1/5] END fselection__k=3, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=2, random_forest__min_samples_split=8;, score=(train=0.839, test=0.719) total time=   2.8s\n",
      "[CV 2/5] END fselection__k=3, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=2, random_forest__min_samples_split=8;, score=(train=0.809, test=0.806) total time=   2.0s\n",
      "[CV 3/5] END fselection__k=3, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=2, random_forest__min_samples_split=8;, score=(train=0.800, test=0.794) total time=   2.9s\n",
      "[CV 4/5] END fselection__k=3, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=2, random_forest__min_samples_split=8;, score=(train=0.827, test=0.775) total time=   2.0s\n",
      "[CV 5/5] END fselection__k=3, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=2, random_forest__min_samples_split=8;, score=(train=0.831, test=0.762) total time=   2.0s\n",
      "[CV 1/5] END fselection__k=4, poly_features__degree=2, random_forest__max_depth=9, random_forest__min_samples_leaf=2, random_forest__min_samples_split=9;, score=(train=0.861, test=0.713) total time=   2.3s\n",
      "[CV 2/5] END fselection__k=4, poly_features__degree=2, random_forest__max_depth=9, random_forest__min_samples_leaf=2, random_forest__min_samples_split=9;, score=(train=0.859, test=0.794) total time=   1.9s\n",
      "[CV 3/5] END fselection__k=4, poly_features__degree=2, random_forest__max_depth=9, random_forest__min_samples_leaf=2, random_forest__min_samples_split=9;, score=(train=0.881, test=0.863) total time=   2.2s\n",
      "[CV 4/5] END fselection__k=4, poly_features__degree=2, random_forest__max_depth=9, random_forest__min_samples_leaf=2, random_forest__min_samples_split=9;, score=(train=0.873, test=0.787) total time=   2.1s\n",
      "[CV 5/5] END fselection__k=4, poly_features__degree=2, random_forest__max_depth=9, random_forest__min_samples_leaf=2, random_forest__min_samples_split=9;, score=(train=0.853, test=0.769) total time=   1.8s\n",
      "[CV 1/5] END fselection__k=8, poly_features__degree=2, random_forest__max_depth=4, random_forest__min_samples_leaf=4, random_forest__min_samples_split=2;, score=(train=0.869, test=0.806) total time=   1.8s\n",
      "[CV 2/5] END fselection__k=8, poly_features__degree=2, random_forest__max_depth=4, random_forest__min_samples_leaf=4, random_forest__min_samples_split=2;, score=(train=0.875, test=0.850) total time=   2.2s\n",
      "[CV 3/5] END fselection__k=8, poly_features__degree=2, random_forest__max_depth=4, random_forest__min_samples_leaf=4, random_forest__min_samples_split=2;, score=(train=0.855, test=0.831) total time=   2.5s\n",
      "[CV 4/5] END fselection__k=8, poly_features__degree=2, random_forest__max_depth=4, random_forest__min_samples_leaf=4, random_forest__min_samples_split=2;, score=(train=0.866, test=0.794) total time=   2.4s\n",
      "[CV 5/5] END fselection__k=8, poly_features__degree=2, random_forest__max_depth=4, random_forest__min_samples_leaf=4, random_forest__min_samples_split=2;, score=(train=0.839, test=0.794) total time=   2.0s\n",
      "[CV 1/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=5, random_forest__min_samples_leaf=4, random_forest__min_samples_split=3;, score=(train=0.886, test=0.844) total time=   1.8s\n",
      "[CV 2/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=5, random_forest__min_samples_leaf=4, random_forest__min_samples_split=3;, score=(train=0.881, test=0.819) total time=   2.3s\n",
      "[CV 3/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=5, random_forest__min_samples_leaf=4, random_forest__min_samples_split=3;, score=(train=0.850, test=0.812) total time=   4.5s\n",
      "[CV 4/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=5, random_forest__min_samples_leaf=4, random_forest__min_samples_split=3;, score=(train=0.867, test=0.800) total time=   3.2s\n",
      "[CV 5/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=5, random_forest__min_samples_leaf=4, random_forest__min_samples_split=3;, score=(train=0.864, test=0.825) total time=   3.2s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=8;, score=(train=0.889, test=0.806) total time=   6.9s\n",
      "[CV 2/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=8;, score=(train=0.897, test=0.825) total time=   5.8s\n",
      "[CV 3/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=8;, score=(train=0.873, test=0.850) total time=   2.8s\n",
      "[CV 4/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=8;, score=(train=0.858, test=0.769) total time=   1.2s\n",
      "[CV 5/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=8;, score=(train=0.878, test=0.844) total time=   1.4s\n",
      "[CV 1/5] END fselection__k=5, poly_features__degree=1, random_forest__max_depth=2, random_forest__min_samples_leaf=3, random_forest__min_samples_split=5;, score=(train=0.827, test=0.806) total time=   0.3s\n",
      "[CV 2/5] END fselection__k=5, poly_features__degree=1, random_forest__max_depth=2, random_forest__min_samples_leaf=3, random_forest__min_samples_split=5;, score=(train=0.827, test=0.838) total time=   0.2s\n",
      "[CV 3/5] END fselection__k=5, poly_features__degree=1, random_forest__max_depth=2, random_forest__min_samples_leaf=3, random_forest__min_samples_split=5;, score=(train=0.811, test=0.844) total time=   0.2s\n",
      "[CV 4/5] END fselection__k=5, poly_features__degree=1, random_forest__max_depth=2, random_forest__min_samples_leaf=3, random_forest__min_samples_split=5;, score=(train=0.841, test=0.781) total time=   0.2s\n",
      "[CV 5/5] END fselection__k=5, poly_features__degree=1, random_forest__max_depth=2, random_forest__min_samples_leaf=3, random_forest__min_samples_split=5;, score=(train=0.830, test=0.800) total time=   0.2s\n",
      "[CV 1/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=5, random_forest__min_samples_leaf=3, random_forest__min_samples_split=2;, score=(train=0.877, test=0.838) total time=   0.2s\n",
      "[CV 2/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=5, random_forest__min_samples_leaf=3, random_forest__min_samples_split=2;, score=(train=0.872, test=0.850) total time=   0.2s\n",
      "[CV 3/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=5, random_forest__min_samples_leaf=3, random_forest__min_samples_split=2;, score=(train=0.867, test=0.844) total time=   0.4s\n",
      "[CV 4/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=5, random_forest__min_samples_leaf=3, random_forest__min_samples_split=2;, score=(train=0.873, test=0.781) total time=   0.4s\n",
      "[CV 5/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=5, random_forest__min_samples_leaf=3, random_forest__min_samples_split=2;, score=(train=0.873, test=0.831) total time=   0.2s\n",
      "[CV 1/5] END fselection__k=9, poly_features__degree=1, random_forest__max_depth=5, random_forest__min_samples_leaf=3, random_forest__min_samples_split=7;, score=(train=0.908, test=0.856) total time=   0.2s\n",
      "[CV 2/5] END fselection__k=9, poly_features__degree=1, random_forest__max_depth=5, random_forest__min_samples_leaf=3, random_forest__min_samples_split=7;, score=(train=0.908, test=0.881) total time=   0.2s\n",
      "[CV 3/5] END fselection__k=9, poly_features__degree=1, random_forest__max_depth=5, random_forest__min_samples_leaf=3, random_forest__min_samples_split=7;, score=(train=0.891, test=0.875) total time=   0.2s\n",
      "[CV 4/5] END fselection__k=9, poly_features__degree=1, random_forest__max_depth=5, random_forest__min_samples_leaf=3, random_forest__min_samples_split=7;, score=(train=0.886, test=0.812) total time=   0.2s\n",
      "[CV 5/5] END fselection__k=9, poly_features__degree=1, random_forest__max_depth=5, random_forest__min_samples_leaf=3, random_forest__min_samples_split=7;, score=(train=0.902, test=0.869) total time=   0.2s\n",
      "[CV 1/5] END fselection__k=7, poly_features__degree=1, random_forest__max_depth=8, random_forest__min_samples_leaf=1, random_forest__min_samples_split=8;, score=(train=0.970, test=0.887) total time=   0.2s\n",
      "[CV 2/5] END fselection__k=7, poly_features__degree=1, random_forest__max_depth=8, random_forest__min_samples_leaf=1, random_forest__min_samples_split=8;, score=(train=0.958, test=0.850) total time=   0.4s\n",
      "[CV 3/5] END fselection__k=7, poly_features__degree=1, random_forest__max_depth=8, random_forest__min_samples_leaf=1, random_forest__min_samples_split=8;, score=(train=0.952, test=0.850) total time=   0.4s\n",
      "[CV 4/5] END fselection__k=7, poly_features__degree=1, random_forest__max_depth=8, random_forest__min_samples_leaf=1, random_forest__min_samples_split=8;, score=(train=0.933, test=0.844) total time=   0.5s\n",
      "[CV 5/5] END fselection__k=7, poly_features__degree=1, random_forest__max_depth=8, random_forest__min_samples_leaf=1, random_forest__min_samples_split=8;, score=(train=0.967, test=0.869) total time=   0.8s\n",
      "[CV 1/5] END fselection__k=7, poly_features__degree=2, random_forest__max_depth=8, random_forest__min_samples_leaf=4, random_forest__min_samples_split=9;, score=(train=0.930, test=0.838) total time=   3.2s\n",
      "[CV 2/5] END fselection__k=7, poly_features__degree=2, random_forest__max_depth=8, random_forest__min_samples_leaf=4, random_forest__min_samples_split=9;, score=(train=0.916, test=0.806) total time=   2.3s\n",
      "[CV 3/5] END fselection__k=7, poly_features__degree=2, random_forest__max_depth=8, random_forest__min_samples_leaf=4, random_forest__min_samples_split=9;, score=(train=0.894, test=0.869) total time=   2.2s\n",
      "[CV 4/5] END fselection__k=7, poly_features__degree=2, random_forest__max_depth=8, random_forest__min_samples_leaf=4, random_forest__min_samples_split=9;, score=(train=0.922, test=0.769) total time=   2.3s\n",
      "[CV 5/5] END fselection__k=7, poly_features__degree=2, random_forest__max_depth=8, random_forest__min_samples_leaf=4, random_forest__min_samples_split=9;, score=(train=0.909, test=0.825) total time=   1.7s\n",
      "[CV 1/5] END fselection__k=7, poly_features__degree=2, random_forest__max_depth=7, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.917, test=0.787) total time=   1.2s\n",
      "[CV 2/5] END fselection__k=7, poly_features__degree=2, random_forest__max_depth=7, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.920, test=0.787) total time=   1.3s\n",
      "[CV 3/5] END fselection__k=7, poly_features__degree=2, random_forest__max_depth=7, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.905, test=0.856) total time=   1.6s\n",
      "[CV 4/5] END fselection__k=7, poly_features__degree=2, random_forest__max_depth=7, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.917, test=0.800) total time=   1.4s\n",
      "[CV 5/5] END fselection__k=7, poly_features__degree=2, random_forest__max_depth=7, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.914, test=0.812) total time=   1.2s\n",
      "[CV 1/5] END fselection__k=8, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=1, random_forest__min_samples_split=7;, score=(train=0.833, test=0.812) total time=   0.2s\n",
      "[CV 2/5] END fselection__k=8, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=1, random_forest__min_samples_split=7;, score=(train=0.848, test=0.856) total time=   0.2s\n",
      "[CV 3/5] END fselection__k=8, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=1, random_forest__min_samples_split=7;, score=(train=0.859, test=0.856) total time=   0.2s\n",
      "[CV 4/5] END fselection__k=8, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=1, random_forest__min_samples_split=7;, score=(train=0.856, test=0.781) total time=   0.2s\n",
      "[CV 5/5] END fselection__k=8, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=1, random_forest__min_samples_split=7;, score=(train=0.836, test=0.825) total time=   0.5s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/5] END fselection__k=7, poly_features__degree=1, random_forest__max_depth=1, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.831, test=0.800) total time=   0.3s\n",
      "[CV 2/5] END fselection__k=7, poly_features__degree=1, random_forest__max_depth=1, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.805, test=0.850) total time=   0.2s\n",
      "[CV 3/5] END fselection__k=7, poly_features__degree=1, random_forest__max_depth=1, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.814, test=0.819) total time=   0.3s\n",
      "[CV 4/5] END fselection__k=7, poly_features__degree=1, random_forest__max_depth=1, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.833, test=0.744) total time=   0.5s\n",
      "[CV 5/5] END fselection__k=7, poly_features__degree=1, random_forest__max_depth=1, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.819, test=0.831) total time=   0.3s\n",
      "[CV 1/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=3, random_forest__min_samples_leaf=4, random_forest__min_samples_split=5;, score=(train=0.844, test=0.781) total time=   1.2s\n",
      "[CV 2/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=3, random_forest__min_samples_leaf=4, random_forest__min_samples_split=5;, score=(train=0.833, test=0.812) total time=   1.2s\n",
      "[CV 3/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=3, random_forest__min_samples_leaf=4, random_forest__min_samples_split=5;, score=(train=0.803, test=0.812) total time=   1.3s\n",
      "[CV 4/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=3, random_forest__min_samples_leaf=4, random_forest__min_samples_split=5;, score=(train=0.842, test=0.781) total time=   1.1s\n",
      "[CV 5/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=3, random_forest__min_samples_leaf=4, random_forest__min_samples_split=5;, score=(train=0.806, test=0.775) total time=   1.6s\n",
      "[CV 1/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=8, random_forest__min_samples_leaf=1, random_forest__min_samples_split=7;, score=(train=0.948, test=0.844) total time=   0.3s\n",
      "[CV 2/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=8, random_forest__min_samples_leaf=1, random_forest__min_samples_split=7;, score=(train=0.930, test=0.844) total time=   0.3s\n",
      "[CV 3/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=8, random_forest__min_samples_leaf=1, random_forest__min_samples_split=7;, score=(train=0.934, test=0.875) total time=   0.3s\n",
      "[CV 4/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=8, random_forest__min_samples_leaf=1, random_forest__min_samples_split=7;, score=(train=0.959, test=0.869) total time=   0.2s\n",
      "[CV 5/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=8, random_forest__min_samples_leaf=1, random_forest__min_samples_split=7;, score=(train=0.944, test=0.887) total time=   0.2s\n",
      "[CV 1/5] END fselection__k=4, poly_features__degree=2, random_forest__max_depth=1, random_forest__min_samples_leaf=4, random_forest__min_samples_split=5;, score=(train=0.783, test=0.781) total time=   1.2s\n",
      "[CV 2/5] END fselection__k=4, poly_features__degree=2, random_forest__max_depth=1, random_forest__min_samples_leaf=4, random_forest__min_samples_split=5;, score=(train=0.775, test=0.812) total time=   1.5s\n",
      "[CV 3/5] END fselection__k=4, poly_features__degree=2, random_forest__max_depth=1, random_forest__min_samples_leaf=4, random_forest__min_samples_split=5;, score=(train=0.780, test=0.794) total time=   1.2s\n",
      "[CV 4/5] END fselection__k=4, poly_features__degree=2, random_forest__max_depth=1, random_forest__min_samples_leaf=4, random_forest__min_samples_split=5;, score=(train=0.791, test=0.750) total time=   1.1s\n",
      "[CV 5/5] END fselection__k=4, poly_features__degree=2, random_forest__max_depth=1, random_forest__min_samples_leaf=4, random_forest__min_samples_split=5;, score=(train=0.784, test=0.775) total time=   1.2s\n",
      "[CV 1/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=1, random_forest__min_samples_leaf=3, random_forest__min_samples_split=5;, score=(train=0.791, test=0.781) total time=   1.2s\n",
      "[CV 2/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=1, random_forest__min_samples_leaf=3, random_forest__min_samples_split=5;, score=(train=0.775, test=0.812) total time=   1.4s\n",
      "[CV 3/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=1, random_forest__min_samples_leaf=3, random_forest__min_samples_split=5;, score=(train=0.787, test=0.794) total time=   1.2s\n",
      "[CV 4/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=1, random_forest__min_samples_leaf=3, random_forest__min_samples_split=5;, score=(train=0.795, test=0.769) total time=   1.2s\n",
      "[CV 5/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=1, random_forest__min_samples_leaf=3, random_forest__min_samples_split=5;, score=(train=0.786, test=0.787) total time=   1.5s\n",
      "[CV 1/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=8, random_forest__min_samples_leaf=2, random_forest__min_samples_split=8;, score=(train=0.959, test=0.831) total time=   0.2s\n",
      "[CV 2/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=8, random_forest__min_samples_leaf=2, random_forest__min_samples_split=8;, score=(train=0.945, test=0.856) total time=   0.3s\n",
      "[CV 3/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=8, random_forest__min_samples_leaf=2, random_forest__min_samples_split=8;, score=(train=0.944, test=0.869) total time=   0.3s\n",
      "[CV 4/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=8, random_forest__min_samples_leaf=2, random_forest__min_samples_split=8;, score=(train=0.939, test=0.869) total time=   0.3s\n",
      "[CV 5/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=8, random_forest__min_samples_leaf=2, random_forest__min_samples_split=8;, score=(train=0.936, test=0.881) total time=   0.3s\n",
      "[CV 1/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=1, random_forest__min_samples_split=8;, score=(train=0.836, test=0.800) total time=   0.2s\n",
      "[CV 2/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=1, random_forest__min_samples_split=8;, score=(train=0.841, test=0.850) total time=   0.2s\n",
      "[CV 3/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=1, random_forest__min_samples_split=8;, score=(train=0.828, test=0.825) total time=   0.2s\n",
      "[CV 4/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=1, random_forest__min_samples_split=8;, score=(train=0.847, test=0.781) total time=   0.2s\n",
      "[CV 5/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=1, random_forest__min_samples_split=8;, score=(train=0.839, test=0.844) total time=   0.2s\n",
      "[CV 1/5] END fselection__k=10, poly_features__degree=1, random_forest__max_depth=9, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.975, test=0.838) total time=   0.2s\n",
      "[CV 2/5] END fselection__k=10, poly_features__degree=1, random_forest__max_depth=9, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.978, test=0.875) total time=   0.2s\n",
      "[CV 3/5] END fselection__k=10, poly_features__degree=1, random_forest__max_depth=9, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.972, test=0.906) total time=   0.2s\n",
      "[CV 4/5] END fselection__k=10, poly_features__degree=1, random_forest__max_depth=9, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.969, test=0.869) total time=   0.3s\n",
      "[CV 5/5] END fselection__k=10, poly_features__degree=1, random_forest__max_depth=9, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.972, test=0.869) total time=   0.2s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/5] END fselection__k=10, poly_features__degree=1, random_forest__max_depth=2, random_forest__min_samples_leaf=2, random_forest__min_samples_split=5;, score=(train=0.834, test=0.787) total time=   0.2s\n",
      "[CV 2/5] END fselection__k=10, poly_features__degree=1, random_forest__max_depth=2, random_forest__min_samples_leaf=2, random_forest__min_samples_split=5;, score=(train=0.823, test=0.844) total time=   0.2s\n",
      "[CV 3/5] END fselection__k=10, poly_features__degree=1, random_forest__max_depth=2, random_forest__min_samples_leaf=2, random_forest__min_samples_split=5;, score=(train=0.825, test=0.825) total time=   0.2s\n",
      "[CV 4/5] END fselection__k=10, poly_features__degree=1, random_forest__max_depth=2, random_forest__min_samples_leaf=2, random_forest__min_samples_split=5;, score=(train=0.848, test=0.787) total time=   0.2s\n",
      "[CV 5/5] END fselection__k=10, poly_features__degree=1, random_forest__max_depth=2, random_forest__min_samples_leaf=2, random_forest__min_samples_split=5;, score=(train=0.833, test=0.831) total time=   0.2s\n",
      "[CV 1/5] END fselection__k=10, poly_features__degree=2, random_forest__max_depth=9, random_forest__min_samples_leaf=4, random_forest__min_samples_split=5;, score=(train=0.938, test=0.825) total time=   1.4s\n",
      "[CV 2/5] END fselection__k=10, poly_features__degree=2, random_forest__max_depth=9, random_forest__min_samples_leaf=4, random_forest__min_samples_split=5;, score=(train=0.941, test=0.819) total time=   1.5s\n",
      "[CV 3/5] END fselection__k=10, poly_features__degree=2, random_forest__max_depth=9, random_forest__min_samples_leaf=4, random_forest__min_samples_split=5;, score=(train=0.934, test=0.863) total time=   1.3s\n",
      "[CV 4/5] END fselection__k=10, poly_features__degree=2, random_forest__max_depth=9, random_forest__min_samples_leaf=4, random_forest__min_samples_split=5;, score=(train=0.944, test=0.819) total time=   1.2s\n",
      "[CV 5/5] END fselection__k=10, poly_features__degree=2, random_forest__max_depth=9, random_forest__min_samples_leaf=4, random_forest__min_samples_split=5;, score=(train=0.939, test=0.831) total time=   1.7s\n",
      "[CV 1/5] END fselection__k=10, poly_features__degree=1, random_forest__max_depth=4, random_forest__min_samples_leaf=3, random_forest__min_samples_split=9;, score=(train=0.867, test=0.794) total time=   0.5s\n",
      "[CV 2/5] END fselection__k=10, poly_features__degree=1, random_forest__max_depth=4, random_forest__min_samples_leaf=3, random_forest__min_samples_split=9;, score=(train=0.870, test=0.863) total time=   0.3s\n",
      "[CV 3/5] END fselection__k=10, poly_features__degree=1, random_forest__max_depth=4, random_forest__min_samples_leaf=3, random_forest__min_samples_split=9;, score=(train=0.861, test=0.850) total time=   0.2s\n",
      "[CV 4/5] END fselection__k=10, poly_features__degree=1, random_forest__max_depth=4, random_forest__min_samples_leaf=3, random_forest__min_samples_split=9;, score=(train=0.873, test=0.800) total time=   0.2s\n",
      "[CV 5/5] END fselection__k=10, poly_features__degree=1, random_forest__max_depth=4, random_forest__min_samples_leaf=3, random_forest__min_samples_split=9;, score=(train=0.878, test=0.869) total time=   0.3s\n",
      "[CV 1/5] END fselection__k=1, poly_features__degree=2, random_forest__max_depth=8, random_forest__min_samples_leaf=2, random_forest__min_samples_split=9;, score=(train=0.830, test=0.713) total time=   1.5s\n",
      "[CV 2/5] END fselection__k=1, poly_features__degree=2, random_forest__max_depth=8, random_forest__min_samples_leaf=2, random_forest__min_samples_split=9;, score=(train=0.823, test=0.775) total time=   1.2s\n",
      "[CV 3/5] END fselection__k=1, poly_features__degree=2, random_forest__max_depth=8, random_forest__min_samples_leaf=2, random_forest__min_samples_split=9;, score=(train=0.808, test=0.794) total time=   1.1s\n",
      "[CV 4/5] END fselection__k=1, poly_features__degree=2, random_forest__max_depth=8, random_forest__min_samples_leaf=2, random_forest__min_samples_split=9;, score=(train=0.828, test=0.750) total time=   1.1s\n",
      "[CV 5/5] END fselection__k=1, poly_features__degree=2, random_forest__max_depth=8, random_forest__min_samples_leaf=2, random_forest__min_samples_split=9;, score=(train=0.838, test=0.731) total time=   1.2s\n",
      "[CV 1/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=2, random_forest__min_samples_split=8;, score=(train=0.836, test=0.806) total time=   0.2s\n",
      "[CV 2/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=2, random_forest__min_samples_split=8;, score=(train=0.842, test=0.850) total time=   0.2s\n",
      "[CV 3/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=2, random_forest__min_samples_split=8;, score=(train=0.830, test=0.825) total time=   0.2s\n",
      "[CV 4/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=2, random_forest__min_samples_split=8;, score=(train=0.847, test=0.781) total time=   0.2s\n",
      "[CV 5/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=2, random_forest__min_samples_split=8;, score=(train=0.841, test=0.844) total time=   0.2s\n",
      "[CV 1/5] END fselection__k=4, poly_features__degree=1, random_forest__max_depth=2, random_forest__min_samples_leaf=1, random_forest__min_samples_split=9;, score=(train=0.822, test=0.794) total time=   0.2s\n",
      "[CV 2/5] END fselection__k=4, poly_features__degree=1, random_forest__max_depth=2, random_forest__min_samples_leaf=1, random_forest__min_samples_split=9;, score=(train=0.822, test=0.831) total time=   0.2s\n",
      "[CV 3/5] END fselection__k=4, poly_features__degree=1, random_forest__max_depth=2, random_forest__min_samples_leaf=1, random_forest__min_samples_split=9;, score=(train=0.816, test=0.850) total time=   0.2s\n",
      "[CV 4/5] END fselection__k=4, poly_features__degree=1, random_forest__max_depth=2, random_forest__min_samples_leaf=1, random_forest__min_samples_split=9;, score=(train=0.828, test=0.775) total time=   0.2s\n",
      "[CV 5/5] END fselection__k=4, poly_features__degree=1, random_forest__max_depth=2, random_forest__min_samples_leaf=1, random_forest__min_samples_split=9;, score=(train=0.828, test=0.800) total time=   0.2s\n",
      "[CV 1/5] END fselection__k=4, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=4, random_forest__min_samples_split=2;, score=(train=0.842, test=0.806) total time=   0.2s\n",
      "[CV 2/5] END fselection__k=4, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=4, random_forest__min_samples_split=2;, score=(train=0.845, test=0.850) total time=   0.2s\n",
      "[CV 3/5] END fselection__k=4, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=4, random_forest__min_samples_split=2;, score=(train=0.838, test=0.844) total time=   0.2s\n",
      "[CV 4/5] END fselection__k=4, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=4, random_forest__min_samples_split=2;, score=(train=0.855, test=0.787) total time=   0.2s\n",
      "[CV 5/5] END fselection__k=4, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=4, random_forest__min_samples_split=2;, score=(train=0.844, test=0.844) total time=   0.2s\n",
      "[CV 1/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=7, random_forest__min_samples_leaf=2, random_forest__min_samples_split=2;, score=(train=0.848, test=0.750) total time=   1.5s\n",
      "[CV 2/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=7, random_forest__min_samples_leaf=2, random_forest__min_samples_split=2;, score=(train=0.830, test=0.812) total time=   1.3s\n",
      "[CV 3/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=7, random_forest__min_samples_leaf=2, random_forest__min_samples_split=2;, score=(train=0.808, test=0.787) total time=   1.2s\n",
      "[CV 4/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=7, random_forest__min_samples_leaf=2, random_forest__min_samples_split=2;, score=(train=0.839, test=0.769) total time=   1.3s\n",
      "[CV 5/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=7, random_forest__min_samples_leaf=2, random_forest__min_samples_split=2;, score=(train=0.844, test=0.762) total time=   1.3s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=3, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.828, test=0.775) total time=   1.4s\n",
      "[CV 2/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=3, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.817, test=0.812) total time=   2.1s\n",
      "[CV 3/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=3, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.808, test=0.812) total time=   1.2s\n",
      "[CV 4/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=3, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.816, test=0.750) total time=   1.2s\n",
      "[CV 5/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=3, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.812, test=0.794) total time=   1.2s\n",
      "[CV 1/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=4, random_forest__min_samples_split=9;, score=(train=0.912, test=0.819) total time=   0.4s\n",
      "[CV 2/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=4, random_forest__min_samples_split=9;, score=(train=0.902, test=0.831) total time=   0.3s\n",
      "[CV 3/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=4, random_forest__min_samples_split=9;, score=(train=0.884, test=0.863) total time=   0.2s\n",
      "[CV 4/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=4, random_forest__min_samples_split=9;, score=(train=0.895, test=0.781) total time=   0.2s\n",
      "[CV 5/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=4, random_forest__min_samples_split=9;, score=(train=0.894, test=0.831) total time=   0.2s\n",
      "[CV 1/5] END fselection__k=1, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=1, random_forest__min_samples_split=5;, score=(train=0.836, test=0.719) total time=   0.2s\n",
      "[CV 2/5] END fselection__k=1, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=1, random_forest__min_samples_split=5;, score=(train=0.830, test=0.775) total time=   0.2s\n",
      "[CV 3/5] END fselection__k=1, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=1, random_forest__min_samples_split=5;, score=(train=0.811, test=0.781) total time=   0.2s\n",
      "[CV 4/5] END fselection__k=1, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=1, random_forest__min_samples_split=5;, score=(train=0.823, test=0.769) total time=   0.3s\n",
      "[CV 5/5] END fselection__k=1, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=1, random_forest__min_samples_split=5;, score=(train=0.831, test=0.744) total time=   0.4s\n",
      "[CV 1/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=1, random_forest__min_samples_split=2;, score=(train=0.902, test=0.800) total time=   1.7s\n",
      "[CV 2/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=1, random_forest__min_samples_split=2;, score=(train=0.900, test=0.812) total time=   3.1s\n",
      "[CV 3/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=1, random_forest__min_samples_split=2;, score=(train=0.891, test=0.844) total time=   1.7s\n",
      "[CV 4/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=1, random_forest__min_samples_split=2;, score=(train=0.897, test=0.794) total time=   1.6s\n",
      "[CV 5/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=1, random_forest__min_samples_split=2;, score=(train=0.891, test=0.812) total time=   2.3s\n",
      "[CV 1/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=4, random_forest__min_samples_leaf=1, random_forest__min_samples_split=4;, score=(train=0.856, test=0.819) total time=   0.4s\n",
      "[CV 2/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=4, random_forest__min_samples_leaf=1, random_forest__min_samples_split=4;, score=(train=0.855, test=0.850) total time=   0.4s\n",
      "[CV 3/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=4, random_forest__min_samples_leaf=1, random_forest__min_samples_split=4;, score=(train=0.853, test=0.844) total time=   0.4s\n",
      "[CV 4/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=4, random_forest__min_samples_leaf=1, random_forest__min_samples_split=4;, score=(train=0.864, test=0.781) total time=   0.2s\n",
      "[CV 5/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=4, random_forest__min_samples_leaf=1, random_forest__min_samples_split=4;, score=(train=0.866, test=0.831) total time=   0.2s\n",
      "[CV 1/5] END fselection__k=1, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=4;, score=(train=0.817, test=0.713) total time=   0.2s\n",
      "[CV 2/5] END fselection__k=1, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=4;, score=(train=0.809, test=0.781) total time=   0.4s\n",
      "[CV 3/5] END fselection__k=1, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=4;, score=(train=0.798, test=0.787) total time=   0.4s\n",
      "[CV 4/5] END fselection__k=1, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=4;, score=(train=0.819, test=0.769) total time=   0.3s\n",
      "[CV 5/5] END fselection__k=1, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=4;, score=(train=0.822, test=0.738) total time=   0.3s\n",
      "[CV 1/5] END fselection__k=7, poly_features__degree=2, random_forest__max_depth=5, random_forest__min_samples_leaf=1, random_forest__min_samples_split=9;, score=(train=0.875, test=0.794) total time=   1.7s\n",
      "[CV 2/5] END fselection__k=7, poly_features__degree=2, random_forest__max_depth=5, random_forest__min_samples_leaf=1, random_forest__min_samples_split=9;, score=(train=0.875, test=0.812) total time=   1.8s\n",
      "[CV 3/5] END fselection__k=7, poly_features__degree=2, random_forest__max_depth=5, random_forest__min_samples_leaf=1, random_forest__min_samples_split=9;, score=(train=0.855, test=0.831) total time=   1.2s\n",
      "[CV 4/5] END fselection__k=7, poly_features__degree=2, random_forest__max_depth=5, random_forest__min_samples_leaf=1, random_forest__min_samples_split=9;, score=(train=0.887, test=0.800) total time=   1.2s\n",
      "[CV 5/5] END fselection__k=7, poly_features__degree=2, random_forest__max_depth=5, random_forest__min_samples_leaf=1, random_forest__min_samples_split=9;, score=(train=0.880, test=0.812) total time=   1.1s\n",
      "[CV 1/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=3, random_forest__min_samples_split=2;, score=(train=0.948, test=0.838) total time=   0.2s\n",
      "[CV 2/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=3, random_forest__min_samples_split=2;, score=(train=0.933, test=0.881) total time=   0.2s\n",
      "[CV 3/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=3, random_forest__min_samples_split=2;, score=(train=0.923, test=0.894) total time=   0.2s\n",
      "[CV 4/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=3, random_forest__min_samples_split=2;, score=(train=0.917, test=0.806) total time=   0.2s\n",
      "[CV 5/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=3, random_forest__min_samples_split=2;, score=(train=0.914, test=0.844) total time=   0.2s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/5] END fselection__k=3, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=2;, score=(train=0.836, test=0.725) total time=   1.1s\n",
      "[CV 2/5] END fselection__k=3, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=2;, score=(train=0.808, test=0.825) total time=   1.1s\n",
      "[CV 3/5] END fselection__k=3, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=2;, score=(train=0.802, test=0.794) total time=   1.2s\n",
      "[CV 4/5] END fselection__k=3, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=2;, score=(train=0.825, test=0.781) total time=   1.1s\n",
      "[CV 5/5] END fselection__k=3, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=2;, score=(train=0.828, test=0.762) total time=   1.1s\n",
      "[CV 1/5] END fselection__k=5, poly_features__degree=1, random_forest__max_depth=1, random_forest__min_samples_leaf=4, random_forest__min_samples_split=6;, score=(train=0.783, test=0.781) total time=   0.2s\n",
      "[CV 2/5] END fselection__k=5, poly_features__degree=1, random_forest__max_depth=1, random_forest__min_samples_leaf=4, random_forest__min_samples_split=6;, score=(train=0.777, test=0.819) total time=   0.2s\n",
      "[CV 3/5] END fselection__k=5, poly_features__degree=1, random_forest__max_depth=1, random_forest__min_samples_leaf=4, random_forest__min_samples_split=6;, score=(train=0.778, test=0.794) total time=   0.2s\n",
      "[CV 4/5] END fselection__k=5, poly_features__degree=1, random_forest__max_depth=1, random_forest__min_samples_leaf=4, random_forest__min_samples_split=6;, score=(train=0.803, test=0.762) total time=   0.2s\n",
      "[CV 5/5] END fselection__k=5, poly_features__degree=1, random_forest__max_depth=1, random_forest__min_samples_leaf=4, random_forest__min_samples_split=6;, score=(train=0.786, test=0.781) total time=   0.2s\n",
      "[CV 1/5] END fselection__k=8, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=2, random_forest__min_samples_split=3;, score=(train=0.855, test=0.806) total time=   0.2s\n",
      "[CV 2/5] END fselection__k=8, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=2, random_forest__min_samples_split=3;, score=(train=0.839, test=0.856) total time=   0.2s\n",
      "[CV 3/5] END fselection__k=8, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=2, random_forest__min_samples_split=3;, score=(train=0.859, test=0.856) total time=   0.2s\n",
      "[CV 4/5] END fselection__k=8, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=2, random_forest__min_samples_split=3;, score=(train=0.856, test=0.806) total time=   0.3s\n",
      "[CV 5/5] END fselection__k=8, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=2, random_forest__min_samples_split=3;, score=(train=0.852, test=0.838) total time=   0.5s\n",
      "[CV 1/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=4, random_forest__min_samples_split=9;, score=(train=0.820, test=0.725) total time=   2.4s\n",
      "[CV 2/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=4, random_forest__min_samples_split=9;, score=(train=0.806, test=0.825) total time=   2.7s\n",
      "[CV 3/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=4, random_forest__min_samples_split=9;, score=(train=0.805, test=0.800) total time=   1.6s\n",
      "[CV 4/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=4, random_forest__min_samples_split=9;, score=(train=0.819, test=0.775) total time=   1.5s\n",
      "[CV 5/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=4, random_forest__min_samples_split=9;, score=(train=0.822, test=0.750) total time=   3.0s\n",
      "[CV 1/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=7;, score=(train=0.900, test=0.781) total time=   1.8s\n",
      "[CV 2/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=7;, score=(train=0.895, test=0.812) total time=   2.1s\n",
      "[CV 3/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=7;, score=(train=0.877, test=0.844) total time=   2.0s\n",
      "[CV 4/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=7;, score=(train=0.889, test=0.812) total time=   1.5s\n",
      "[CV 5/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=3, random_forest__min_samples_split=7;, score=(train=0.883, test=0.819) total time=   1.2s\n",
      "[CV 1/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=2, random_forest__min_samples_split=3;, score=(train=0.894, test=0.781) total time=   1.2s\n",
      "[CV 2/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=2, random_forest__min_samples_split=3;, score=(train=0.911, test=0.812) total time=   1.1s\n",
      "[CV 3/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=2, random_forest__min_samples_split=3;, score=(train=0.878, test=0.863) total time=   1.1s\n",
      "[CV 4/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=2, random_forest__min_samples_split=3;, score=(train=0.889, test=0.794) total time=   1.6s\n",
      "[CV 5/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=2, random_forest__min_samples_split=3;, score=(train=0.895, test=0.819) total time=   1.2s\n",
      "[CV 1/5] END fselection__k=9, poly_features__degree=1, random_forest__max_depth=5, random_forest__min_samples_leaf=1, random_forest__min_samples_split=9;, score=(train=0.916, test=0.850) total time=   0.2s\n",
      "[CV 2/5] END fselection__k=9, poly_features__degree=1, random_forest__max_depth=5, random_forest__min_samples_leaf=1, random_forest__min_samples_split=9;, score=(train=0.891, test=0.850) total time=   0.2s\n",
      "[CV 3/5] END fselection__k=9, poly_features__degree=1, random_forest__max_depth=5, random_forest__min_samples_leaf=1, random_forest__min_samples_split=9;, score=(train=0.905, test=0.869) total time=   0.2s\n",
      "[CV 4/5] END fselection__k=9, poly_features__degree=1, random_forest__max_depth=5, random_forest__min_samples_leaf=1, random_forest__min_samples_split=9;, score=(train=0.905, test=0.825) total time=   0.2s\n",
      "[CV 5/5] END fselection__k=9, poly_features__degree=1, random_forest__max_depth=5, random_forest__min_samples_leaf=1, random_forest__min_samples_split=9;, score=(train=0.902, test=0.863) total time=   0.2s\n",
      "[CV 1/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=3, random_forest__min_samples_leaf=1, random_forest__min_samples_split=8;, score=(train=0.828, test=0.775) total time=   1.2s\n",
      "[CV 2/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=3, random_forest__min_samples_leaf=1, random_forest__min_samples_split=8;, score=(train=0.817, test=0.812) total time=   1.4s\n",
      "[CV 3/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=3, random_forest__min_samples_leaf=1, random_forest__min_samples_split=8;, score=(train=0.808, test=0.812) total time=   1.4s\n",
      "[CV 4/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=3, random_forest__min_samples_leaf=1, random_forest__min_samples_split=8;, score=(train=0.817, test=0.744) total time=   1.7s\n",
      "[CV 5/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=3, random_forest__min_samples_leaf=1, random_forest__min_samples_split=8;, score=(train=0.812, test=0.794) total time=   1.6s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/5] END fselection__k=2, poly_features__degree=1, random_forest__max_depth=9, random_forest__min_samples_leaf=1, random_forest__min_samples_split=9;, score=(train=0.916, test=0.775) total time=   0.2s\n",
      "[CV 2/5] END fselection__k=2, poly_features__degree=1, random_forest__max_depth=9, random_forest__min_samples_leaf=1, random_forest__min_samples_split=9;, score=(train=0.912, test=0.794) total time=   0.2s\n",
      "[CV 3/5] END fselection__k=2, poly_features__degree=1, random_forest__max_depth=9, random_forest__min_samples_leaf=1, random_forest__min_samples_split=9;, score=(train=0.892, test=0.850) total time=   0.2s\n",
      "[CV 4/5] END fselection__k=2, poly_features__degree=1, random_forest__max_depth=9, random_forest__min_samples_leaf=1, random_forest__min_samples_split=9;, score=(train=0.881, test=0.787) total time=   0.3s\n",
      "[CV 5/5] END fselection__k=2, poly_features__degree=1, random_forest__max_depth=9, random_forest__min_samples_leaf=1, random_forest__min_samples_split=9;, score=(train=0.906, test=0.769) total time=   0.7s\n",
      "[CV 1/5] END fselection__k=5, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=2, random_forest__min_samples_split=9;, score=(train=0.934, test=0.838) total time=   0.7s\n",
      "[CV 2/5] END fselection__k=5, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=2, random_forest__min_samples_split=9;, score=(train=0.917, test=0.831) total time=   0.4s\n",
      "[CV 3/5] END fselection__k=5, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=2, random_forest__min_samples_split=9;, score=(train=0.906, test=0.875) total time=   0.2s\n",
      "[CV 4/5] END fselection__k=5, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=2, random_forest__min_samples_split=9;, score=(train=0.916, test=0.831) total time=   0.3s\n",
      "[CV 5/5] END fselection__k=5, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=2, random_forest__min_samples_split=9;, score=(train=0.911, test=0.844) total time=   0.2s\n",
      "[CV 1/5] END fselection__k=4, poly_features__degree=2, random_forest__max_depth=7, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.861, test=0.725) total time=   1.1s\n",
      "[CV 2/5] END fselection__k=4, poly_features__degree=2, random_forest__max_depth=7, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.845, test=0.794) total time=   1.2s\n",
      "[CV 3/5] END fselection__k=4, poly_features__degree=2, random_forest__max_depth=7, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.864, test=0.825) total time=   1.2s\n",
      "[CV 4/5] END fselection__k=4, poly_features__degree=2, random_forest__max_depth=7, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.866, test=0.787) total time=   1.6s\n",
      "[CV 5/5] END fselection__k=4, poly_features__degree=2, random_forest__max_depth=7, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.852, test=0.781) total time=   1.8s\n",
      "[CV 1/5] END fselection__k=8, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=1, random_forest__min_samples_split=4;, score=(train=0.955, test=0.850) total time=   1.0s\n",
      "[CV 2/5] END fselection__k=8, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=1, random_forest__min_samples_split=4;, score=(train=0.938, test=0.869) total time=   0.6s\n",
      "[CV 3/5] END fselection__k=8, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=1, random_forest__min_samples_split=4;, score=(train=0.952, test=0.906) total time=   0.5s\n",
      "[CV 4/5] END fselection__k=8, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=1, random_forest__min_samples_split=4;, score=(train=0.938, test=0.838) total time=   0.5s\n",
      "[CV 5/5] END fselection__k=8, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=1, random_forest__min_samples_split=4;, score=(train=0.936, test=0.875) total time=   0.5s\n",
      "[CV 1/5] END fselection__k=7, poly_features__degree=2, random_forest__max_depth=9, random_forest__min_samples_leaf=3, random_forest__min_samples_split=4;, score=(train=0.938, test=0.825) total time=   2.3s\n",
      "[CV 2/5] END fselection__k=7, poly_features__degree=2, random_forest__max_depth=9, random_forest__min_samples_leaf=3, random_forest__min_samples_split=4;, score=(train=0.925, test=0.787) total time=   3.4s\n",
      "[CV 3/5] END fselection__k=7, poly_features__degree=2, random_forest__max_depth=9, random_forest__min_samples_leaf=3, random_forest__min_samples_split=4;, score=(train=0.914, test=0.869) total time=   2.3s\n",
      "[CV 4/5] END fselection__k=7, poly_features__degree=2, random_forest__max_depth=9, random_forest__min_samples_leaf=3, random_forest__min_samples_split=4;, score=(train=0.936, test=0.806) total time=   1.5s\n",
      "[CV 5/5] END fselection__k=7, poly_features__degree=2, random_forest__max_depth=9, random_forest__min_samples_leaf=3, random_forest__min_samples_split=4;, score=(train=0.914, test=0.812) total time=   1.2s\n",
      "[CV 1/5] END fselection__k=10, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=4, random_forest__min_samples_split=6;, score=(train=0.867, test=0.812) total time=   0.2s\n",
      "[CV 2/5] END fselection__k=10, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=4, random_forest__min_samples_split=6;, score=(train=0.844, test=0.863) total time=   0.2s\n",
      "[CV 3/5] END fselection__k=10, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=4, random_forest__min_samples_split=6;, score=(train=0.848, test=0.838) total time=   0.2s\n",
      "[CV 4/5] END fselection__k=10, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=4, random_forest__min_samples_split=6;, score=(train=0.855, test=0.794) total time=   0.2s\n",
      "[CV 5/5] END fselection__k=10, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=4, random_forest__min_samples_split=6;, score=(train=0.870, test=0.844) total time=   0.2s\n",
      "[CV 1/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=9, random_forest__min_samples_leaf=1, random_forest__min_samples_split=8;, score=(train=0.947, test=0.819) total time=   0.2s\n",
      "[CV 2/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=9, random_forest__min_samples_leaf=1, random_forest__min_samples_split=8;, score=(train=0.933, test=0.819) total time=   0.2s\n",
      "[CV 3/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=9, random_forest__min_samples_leaf=1, random_forest__min_samples_split=8;, score=(train=0.930, test=0.850) total time=   0.2s\n",
      "[CV 4/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=9, random_forest__min_samples_leaf=1, random_forest__min_samples_split=8;, score=(train=0.931, test=0.819) total time=   0.2s\n",
      "[CV 5/5] END fselection__k=3, poly_features__degree=1, random_forest__max_depth=9, random_forest__min_samples_leaf=1, random_forest__min_samples_split=8;, score=(train=0.944, test=0.819) total time=   0.2s\n",
      "[CV 1/5] END fselection__k=9, poly_features__degree=1, random_forest__max_depth=8, random_forest__min_samples_leaf=2, random_forest__min_samples_split=2;, score=(train=0.969, test=0.825) total time=   0.2s\n",
      "[CV 2/5] END fselection__k=9, poly_features__degree=1, random_forest__max_depth=8, random_forest__min_samples_leaf=2, random_forest__min_samples_split=2;, score=(train=0.966, test=0.894) total time=   0.2s\n",
      "[CV 3/5] END fselection__k=9, poly_features__degree=1, random_forest__max_depth=8, random_forest__min_samples_leaf=2, random_forest__min_samples_split=2;, score=(train=0.978, test=0.869) total time=   0.2s\n",
      "[CV 4/5] END fselection__k=9, poly_features__degree=1, random_forest__max_depth=8, random_forest__min_samples_leaf=2, random_forest__min_samples_split=2;, score=(train=0.956, test=0.887) total time=   0.2s\n",
      "[CV 5/5] END fselection__k=9, poly_features__degree=1, random_forest__max_depth=8, random_forest__min_samples_leaf=2, random_forest__min_samples_split=2;, score=(train=0.972, test=0.881) total time=   0.2s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=2, random_forest__min_samples_leaf=4, random_forest__min_samples_split=7;, score=(train=0.784, test=0.769) total time=   1.1s\n",
      "[CV 2/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=2, random_forest__min_samples_leaf=4, random_forest__min_samples_split=7;, score=(train=0.784, test=0.812) total time=   1.5s\n",
      "[CV 3/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=2, random_forest__min_samples_leaf=4, random_forest__min_samples_split=7;, score=(train=0.784, test=0.794) total time=   1.3s\n",
      "[CV 4/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=2, random_forest__min_samples_leaf=4, random_forest__min_samples_split=7;, score=(train=0.805, test=0.756) total time=   1.2s\n",
      "[CV 5/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=2, random_forest__min_samples_leaf=4, random_forest__min_samples_split=7;, score=(train=0.787, test=0.775) total time=   1.1s\n",
      "[CV 1/5] END fselection__k=10, poly_features__degree=1, random_forest__max_depth=1, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.786, test=0.800) total time=   0.2s\n",
      "[CV 2/5] END fselection__k=10, poly_features__degree=1, random_forest__max_depth=1, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.778, test=0.825) total time=   0.2s\n",
      "[CV 3/5] END fselection__k=10, poly_features__degree=1, random_forest__max_depth=1, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.789, test=0.800) total time=   0.2s\n",
      "[CV 4/5] END fselection__k=10, poly_features__degree=1, random_forest__max_depth=1, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.792, test=0.750) total time=   0.2s\n",
      "[CV 5/5] END fselection__k=10, poly_features__degree=1, random_forest__max_depth=1, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.797, test=0.812) total time=   0.4s\n",
      "[CV 1/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.831, test=0.738) total time=   1.7s\n",
      "[CV 2/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.803, test=0.825) total time=   2.9s\n",
      "[CV 3/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.802, test=0.775) total time=   2.2s\n",
      "[CV 4/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.828, test=0.769) total time=   1.8s\n",
      "[CV 5/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.839, test=0.744) total time=   2.4s\n",
      "[CV 1/5] END fselection__k=4, poly_features__degree=2, random_forest__max_depth=3, random_forest__min_samples_leaf=1, random_forest__min_samples_split=2;, score=(train=0.803, test=0.781) total time=   1.2s\n",
      "[CV 2/5] END fselection__k=4, poly_features__degree=2, random_forest__max_depth=3, random_forest__min_samples_leaf=1, random_forest__min_samples_split=2;, score=(train=0.805, test=0.806) total time=   1.1s\n",
      "[CV 3/5] END fselection__k=4, poly_features__degree=2, random_forest__max_depth=3, random_forest__min_samples_leaf=1, random_forest__min_samples_split=2;, score=(train=0.795, test=0.812) total time=   1.2s\n",
      "[CV 4/5] END fselection__k=4, poly_features__degree=2, random_forest__max_depth=3, random_forest__min_samples_leaf=1, random_forest__min_samples_split=2;, score=(train=0.814, test=0.744) total time=   1.4s\n",
      "[CV 5/5] END fselection__k=4, poly_features__degree=2, random_forest__max_depth=3, random_forest__min_samples_leaf=1, random_forest__min_samples_split=2;, score=(train=0.803, test=0.794) total time=   1.5s\n",
      "[CV 1/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=1, random_forest__min_samples_split=3;, score=(train=0.952, test=0.838) total time=   0.2s\n",
      "[CV 2/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=1, random_forest__min_samples_split=3;, score=(train=0.919, test=0.819) total time=   0.2s\n",
      "[CV 3/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=1, random_forest__min_samples_split=3;, score=(train=0.947, test=0.881) total time=   0.2s\n",
      "[CV 4/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=1, random_forest__min_samples_split=3;, score=(train=0.948, test=0.856) total time=   0.2s\n",
      "[CV 5/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=1, random_forest__min_samples_split=3;, score=(train=0.931, test=0.863) total time=   0.2s\n",
      "[CV 1/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=9, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.859, test=0.719) total time=   1.2s\n",
      "[CV 2/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=9, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.848, test=0.794) total time=   1.1s\n",
      "[CV 3/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=9, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.842, test=0.812) total time=   1.4s\n",
      "[CV 4/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=9, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.858, test=0.744) total time=   1.7s\n",
      "[CV 5/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=9, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.864, test=0.738) total time=   3.7s\n",
      "[CV 1/5] END fselection__k=8, poly_features__degree=2, random_forest__max_depth=8, random_forest__min_samples_leaf=4, random_forest__min_samples_split=4;, score=(train=0.928, test=0.806) total time=   3.9s\n",
      "[CV 2/5] END fselection__k=8, poly_features__degree=2, random_forest__max_depth=8, random_forest__min_samples_leaf=4, random_forest__min_samples_split=4;, score=(train=0.922, test=0.825) total time=   2.8s\n",
      "[CV 3/5] END fselection__k=8, poly_features__degree=2, random_forest__max_depth=8, random_forest__min_samples_leaf=4, random_forest__min_samples_split=4;, score=(train=0.923, test=0.869) total time=   3.6s\n",
      "[CV 4/5] END fselection__k=8, poly_features__degree=2, random_forest__max_depth=8, random_forest__min_samples_leaf=4, random_forest__min_samples_split=4;, score=(train=0.922, test=0.806) total time=   2.8s\n",
      "[CV 5/5] END fselection__k=8, poly_features__degree=2, random_forest__max_depth=8, random_forest__min_samples_leaf=4, random_forest__min_samples_split=4;, score=(train=0.903, test=0.812) total time=   3.8s\n",
      "[CV 1/5] END fselection__k=8, poly_features__degree=2, random_forest__max_depth=1, random_forest__min_samples_leaf=3, random_forest__min_samples_split=2;, score=(train=0.784, test=0.769) total time=   1.8s\n",
      "[CV 2/5] END fselection__k=8, poly_features__degree=2, random_forest__max_depth=1, random_forest__min_samples_leaf=3, random_forest__min_samples_split=2;, score=(train=0.775, test=0.812) total time=   1.4s\n",
      "[CV 3/5] END fselection__k=8, poly_features__degree=2, random_forest__max_depth=1, random_forest__min_samples_leaf=3, random_forest__min_samples_split=2;, score=(train=0.780, test=0.794) total time=   3.0s\n",
      "[CV 4/5] END fselection__k=8, poly_features__degree=2, random_forest__max_depth=1, random_forest__min_samples_leaf=3, random_forest__min_samples_split=2;, score=(train=0.802, test=0.769) total time=   2.8s\n",
      "[CV 5/5] END fselection__k=8, poly_features__degree=2, random_forest__max_depth=1, random_forest__min_samples_leaf=3, random_forest__min_samples_split=2;, score=(train=0.784, test=0.775) total time=   2.8s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/5] END fselection__k=8, poly_features__degree=2, random_forest__max_depth=9, random_forest__min_samples_leaf=4, random_forest__min_samples_split=3;, score=(train=0.933, test=0.812) total time=   3.2s\n",
      "[CV 2/5] END fselection__k=8, poly_features__degree=2, random_forest__max_depth=9, random_forest__min_samples_leaf=4, random_forest__min_samples_split=3;, score=(train=0.933, test=0.819) total time=   4.2s\n",
      "[CV 3/5] END fselection__k=8, poly_features__degree=2, random_forest__max_depth=9, random_forest__min_samples_leaf=4, random_forest__min_samples_split=3;, score=(train=0.927, test=0.869) total time=   3.7s\n",
      "[CV 4/5] END fselection__k=8, poly_features__degree=2, random_forest__max_depth=9, random_forest__min_samples_leaf=4, random_forest__min_samples_split=3;, score=(train=0.922, test=0.794) total time=   3.8s\n",
      "[CV 5/5] END fselection__k=8, poly_features__degree=2, random_forest__max_depth=9, random_forest__min_samples_leaf=4, random_forest__min_samples_split=3;, score=(train=0.914, test=0.819) total time=   4.3s\n",
      "[CV 1/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=1, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.783, test=0.781) total time=   4.0s\n",
      "[CV 2/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=1, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.781, test=0.812) total time=   3.2s\n",
      "[CV 3/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=1, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.787, test=0.794) total time=   3.2s\n",
      "[CV 4/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=1, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.794, test=0.744) total time=   1.7s\n",
      "[CV 5/5] END fselection__k=5, poly_features__degree=2, random_forest__max_depth=1, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.784, test=0.775) total time=   2.3s\n",
      "[CV 1/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=8, random_forest__min_samples_leaf=3, random_forest__min_samples_split=5;, score=(train=0.916, test=0.781) total time=   2.0s\n",
      "[CV 2/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=8, random_forest__min_samples_leaf=3, random_forest__min_samples_split=5;, score=(train=0.922, test=0.806) total time=   4.0s\n",
      "[CV 3/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=8, random_forest__min_samples_leaf=3, random_forest__min_samples_split=5;, score=(train=0.895, test=0.863) total time=   1.7s\n",
      "[CV 4/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=8, random_forest__min_samples_leaf=3, random_forest__min_samples_split=5;, score=(train=0.928, test=0.831) total time=   1.1s\n",
      "[CV 5/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=8, random_forest__min_samples_leaf=3, random_forest__min_samples_split=5;, score=(train=0.903, test=0.825) total time=   1.0s\n",
      "[CV 1/5] END fselection__k=8, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=3, random_forest__min_samples_split=8;, score=(train=0.845, test=0.806) total time=   0.2s\n",
      "[CV 2/5] END fselection__k=8, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=3, random_forest__min_samples_split=8;, score=(train=0.842, test=0.875) total time=   0.2s\n",
      "[CV 3/5] END fselection__k=8, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=3, random_forest__min_samples_split=8;, score=(train=0.841, test=0.831) total time=   0.2s\n",
      "[CV 4/5] END fselection__k=8, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=3, random_forest__min_samples_split=8;, score=(train=0.845, test=0.762) total time=   0.2s\n",
      "[CV 5/5] END fselection__k=8, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=3, random_forest__min_samples_split=8;, score=(train=0.850, test=0.812) total time=   0.3s\n",
      "[CV 1/5] END fselection__k=9, poly_features__degree=2, random_forest__max_depth=4, random_forest__min_samples_leaf=1, random_forest__min_samples_split=3;, score=(train=0.870, test=0.794) total time=   1.3s\n",
      "[CV 2/5] END fselection__k=9, poly_features__degree=2, random_forest__max_depth=4, random_forest__min_samples_leaf=1, random_forest__min_samples_split=3;, score=(train=0.881, test=0.844) total time=   1.1s\n",
      "[CV 3/5] END fselection__k=9, poly_features__degree=2, random_forest__max_depth=4, random_forest__min_samples_leaf=1, random_forest__min_samples_split=3;, score=(train=0.866, test=0.856) total time=   1.2s\n",
      "[CV 4/5] END fselection__k=9, poly_features__degree=2, random_forest__max_depth=4, random_forest__min_samples_leaf=1, random_forest__min_samples_split=3;, score=(train=0.877, test=0.800) total time=   2.4s\n",
      "[CV 5/5] END fselection__k=9, poly_features__degree=2, random_forest__max_depth=4, random_forest__min_samples_leaf=1, random_forest__min_samples_split=3;, score=(train=0.877, test=0.812) total time=   1.2s\n",
      "[CV 1/5] END fselection__k=2, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=3, random_forest__min_samples_split=7;, score=(train=0.883, test=0.756) total time=   0.3s\n",
      "[CV 2/5] END fselection__k=2, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=3, random_forest__min_samples_split=7;, score=(train=0.883, test=0.806) total time=   0.4s\n",
      "[CV 3/5] END fselection__k=2, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=3, random_forest__min_samples_split=7;, score=(train=0.864, test=0.844) total time=   0.3s\n",
      "[CV 4/5] END fselection__k=2, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=3, random_forest__min_samples_split=7;, score=(train=0.866, test=0.794) total time=   0.3s\n",
      "[CV 5/5] END fselection__k=2, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=3, random_forest__min_samples_split=7;, score=(train=0.877, test=0.781) total time=   0.4s\n",
      "[CV 1/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.909, test=0.825) total time=   0.3s\n",
      "[CV 2/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.911, test=0.844) total time=   0.2s\n",
      "[CV 3/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.905, test=0.863) total time=   0.3s\n",
      "[CV 4/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.920, test=0.806) total time=   0.5s\n",
      "[CV 5/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=3, random_forest__min_samples_split=3;, score=(train=0.919, test=0.844) total time=   0.3s\n",
      "[CV 1/5] END fselection__k=10, poly_features__degree=2, random_forest__max_depth=9, random_forest__min_samples_leaf=1, random_forest__min_samples_split=7;, score=(train=0.963, test=0.850) total time=   1.2s\n",
      "[CV 2/5] END fselection__k=10, poly_features__degree=2, random_forest__max_depth=9, random_forest__min_samples_leaf=1, random_forest__min_samples_split=7;, score=(train=0.961, test=0.819) total time=   1.1s\n",
      "[CV 3/5] END fselection__k=10, poly_features__degree=2, random_forest__max_depth=9, random_forest__min_samples_leaf=1, random_forest__min_samples_split=7;, score=(train=0.961, test=0.875) total time=   1.1s\n",
      "[CV 4/5] END fselection__k=10, poly_features__degree=2, random_forest__max_depth=9, random_forest__min_samples_leaf=1, random_forest__min_samples_split=7;, score=(train=0.956, test=0.800) total time=   1.6s\n",
      "[CV 5/5] END fselection__k=10, poly_features__degree=2, random_forest__max_depth=9, random_forest__min_samples_leaf=1, random_forest__min_samples_split=7;, score=(train=0.963, test=0.831) total time=   1.9s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/5] END fselection__k=1, poly_features__degree=1, random_forest__max_depth=1, random_forest__min_samples_leaf=1, random_forest__min_samples_split=9;, score=(train=0.783, test=0.781) total time=   0.4s\n",
      "[CV 2/5] END fselection__k=1, poly_features__degree=1, random_forest__max_depth=1, random_forest__min_samples_leaf=1, random_forest__min_samples_split=9;, score=(train=0.775, test=0.812) total time=   0.4s\n",
      "[CV 3/5] END fselection__k=1, poly_features__degree=1, random_forest__max_depth=1, random_forest__min_samples_leaf=1, random_forest__min_samples_split=9;, score=(train=0.780, test=0.794) total time=   0.5s\n",
      "[CV 4/5] END fselection__k=1, poly_features__degree=1, random_forest__max_depth=1, random_forest__min_samples_leaf=1, random_forest__min_samples_split=9;, score=(train=0.791, test=0.750) total time=   0.4s\n",
      "[CV 5/5] END fselection__k=1, poly_features__degree=1, random_forest__max_depth=1, random_forest__min_samples_leaf=1, random_forest__min_samples_split=9;, score=(train=0.784, test=0.775) total time=   0.3s\n",
      "[CV 1/5] END fselection__k=8, poly_features__degree=2, random_forest__max_depth=5, random_forest__min_samples_leaf=4, random_forest__min_samples_split=4;, score=(train=0.884, test=0.819) total time=   1.2s\n",
      "[CV 2/5] END fselection__k=8, poly_features__degree=2, random_forest__max_depth=5, random_forest__min_samples_leaf=4, random_forest__min_samples_split=4;, score=(train=0.895, test=0.863) total time=   1.1s\n",
      "[CV 3/5] END fselection__k=8, poly_features__degree=2, random_forest__max_depth=5, random_forest__min_samples_leaf=4, random_forest__min_samples_split=4;, score=(train=0.875, test=0.856) total time=   1.8s\n",
      "[CV 4/5] END fselection__k=8, poly_features__degree=2, random_forest__max_depth=5, random_forest__min_samples_leaf=4, random_forest__min_samples_split=4;, score=(train=0.878, test=0.806) total time=   1.3s\n",
      "[CV 5/5] END fselection__k=8, poly_features__degree=2, random_forest__max_depth=5, random_forest__min_samples_leaf=4, random_forest__min_samples_split=4;, score=(train=0.869, test=0.806) total time=   1.1s\n",
      "[CV 1/5] END fselection__k=8, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=1, random_forest__min_samples_split=8;, score=(train=0.931, test=0.869) total time=   0.2s\n",
      "[CV 2/5] END fselection__k=8, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=1, random_forest__min_samples_split=8;, score=(train=0.906, test=0.838) total time=   0.2s\n",
      "[CV 3/5] END fselection__k=8, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=1, random_forest__min_samples_split=8;, score=(train=0.911, test=0.844) total time=   0.2s\n",
      "[CV 4/5] END fselection__k=8, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=1, random_forest__min_samples_split=8;, score=(train=0.909, test=0.806) total time=   0.2s\n",
      "[CV 5/5] END fselection__k=8, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=1, random_forest__min_samples_split=8;, score=(train=0.922, test=0.875) total time=   0.2s\n",
      "[CV 1/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=1, random_forest__min_samples_leaf=4, random_forest__min_samples_split=4;, score=(train=0.822, test=0.794) total time=   0.3s\n",
      "[CV 2/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=1, random_forest__min_samples_leaf=4, random_forest__min_samples_split=4;, score=(train=0.809, test=0.844) total time=   0.3s\n",
      "[CV 3/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=1, random_forest__min_samples_leaf=4, random_forest__min_samples_split=4;, score=(train=0.814, test=0.831) total time=   0.2s\n",
      "[CV 4/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=1, random_forest__min_samples_leaf=4, random_forest__min_samples_split=4;, score=(train=0.822, test=0.750) total time=   0.6s\n",
      "[CV 5/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=1, random_forest__min_samples_leaf=4, random_forest__min_samples_split=4;, score=(train=0.811, test=0.800) total time=   0.4s\n",
      "[CV 1/5] END fselection__k=7, poly_features__degree=2, random_forest__max_depth=1, random_forest__min_samples_leaf=4, random_forest__min_samples_split=4;, score=(train=0.783, test=0.781) total time=   1.8s\n",
      "[CV 2/5] END fselection__k=7, poly_features__degree=2, random_forest__max_depth=1, random_forest__min_samples_leaf=4, random_forest__min_samples_split=4;, score=(train=0.775, test=0.812) total time=   1.5s\n",
      "[CV 3/5] END fselection__k=7, poly_features__degree=2, random_forest__max_depth=1, random_forest__min_samples_leaf=4, random_forest__min_samples_split=4;, score=(train=0.781, test=0.794) total time=   2.2s\n",
      "[CV 4/5] END fselection__k=7, poly_features__degree=2, random_forest__max_depth=1, random_forest__min_samples_leaf=4, random_forest__min_samples_split=4;, score=(train=0.791, test=0.750) total time=   3.4s\n",
      "[CV 5/5] END fselection__k=7, poly_features__degree=2, random_forest__max_depth=1, random_forest__min_samples_leaf=4, random_forest__min_samples_split=4;, score=(train=0.784, test=0.775) total time=   1.8s\n",
      "[CV 1/5] END fselection__k=9, poly_features__degree=1, random_forest__max_depth=4, random_forest__min_samples_leaf=4, random_forest__min_samples_split=9;, score=(train=0.881, test=0.819) total time=   0.3s\n",
      "[CV 2/5] END fselection__k=9, poly_features__degree=1, random_forest__max_depth=4, random_forest__min_samples_leaf=4, random_forest__min_samples_split=9;, score=(train=0.858, test=0.863) total time=   0.3s\n",
      "[CV 3/5] END fselection__k=9, poly_features__degree=1, random_forest__max_depth=4, random_forest__min_samples_leaf=4, random_forest__min_samples_split=9;, score=(train=0.880, test=0.850) total time=   0.2s\n",
      "[CV 4/5] END fselection__k=9, poly_features__degree=1, random_forest__max_depth=4, random_forest__min_samples_leaf=4, random_forest__min_samples_split=9;, score=(train=0.864, test=0.800) total time=   0.2s\n",
      "[CV 5/5] END fselection__k=9, poly_features__degree=1, random_forest__max_depth=4, random_forest__min_samples_leaf=4, random_forest__min_samples_split=9;, score=(train=0.869, test=0.831) total time=   0.2s\n",
      "[CV 1/5] END fselection__k=9, poly_features__degree=2, random_forest__max_depth=2, random_forest__min_samples_leaf=3, random_forest__min_samples_split=9;, score=(train=0.802, test=0.781) total time=   1.0s\n",
      "[CV 2/5] END fselection__k=9, poly_features__degree=2, random_forest__max_depth=2, random_forest__min_samples_leaf=3, random_forest__min_samples_split=9;, score=(train=0.783, test=0.825) total time=   1.0s\n",
      "[CV 3/5] END fselection__k=9, poly_features__degree=2, random_forest__max_depth=2, random_forest__min_samples_leaf=3, random_forest__min_samples_split=9;, score=(train=0.780, test=0.794) total time=   1.0s\n",
      "[CV 4/5] END fselection__k=9, poly_features__degree=2, random_forest__max_depth=2, random_forest__min_samples_leaf=3, random_forest__min_samples_split=9;, score=(train=0.827, test=0.756) total time=   1.9s\n",
      "[CV 5/5] END fselection__k=9, poly_features__degree=2, random_forest__max_depth=2, random_forest__min_samples_leaf=3, random_forest__min_samples_split=9;, score=(train=0.803, test=0.775) total time=   2.4s\n",
      "[CV 1/5] END fselection__k=5, poly_features__degree=1, random_forest__max_depth=8, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.964, test=0.819) total time=   0.3s\n",
      "[CV 2/5] END fselection__k=5, poly_features__degree=1, random_forest__max_depth=8, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.953, test=0.844) total time=   0.3s\n",
      "[CV 3/5] END fselection__k=5, poly_features__degree=1, random_forest__max_depth=8, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.948, test=0.875) total time=   0.4s\n",
      "[CV 4/5] END fselection__k=5, poly_features__degree=1, random_forest__max_depth=8, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.948, test=0.819) total time=   0.5s\n",
      "[CV 5/5] END fselection__k=5, poly_features__degree=1, random_forest__max_depth=8, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.945, test=0.838) total time=   0.5s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/5] END fselection__k=10, poly_features__degree=1, random_forest__max_depth=5, random_forest__min_samples_leaf=3, random_forest__min_samples_split=9;, score=(train=0.914, test=0.863) total time=   0.6s\n",
      "[CV 2/5] END fselection__k=10, poly_features__degree=1, random_forest__max_depth=5, random_forest__min_samples_leaf=3, random_forest__min_samples_split=9;, score=(train=0.877, test=0.863) total time=   0.8s\n",
      "[CV 3/5] END fselection__k=10, poly_features__degree=1, random_forest__max_depth=5, random_forest__min_samples_leaf=3, random_forest__min_samples_split=9;, score=(train=0.903, test=0.894) total time=   0.9s\n",
      "[CV 4/5] END fselection__k=10, poly_features__degree=1, random_forest__max_depth=5, random_forest__min_samples_leaf=3, random_forest__min_samples_split=9;, score=(train=0.892, test=0.812) total time=   0.4s\n",
      "[CV 5/5] END fselection__k=10, poly_features__degree=1, random_forest__max_depth=5, random_forest__min_samples_leaf=3, random_forest__min_samples_split=9;, score=(train=0.916, test=0.881) total time=   0.2s\n",
      "[CV 1/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=2, random_forest__min_samples_split=7;, score=(train=0.863, test=0.825) total time=   0.3s\n",
      "[CV 2/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=2, random_forest__min_samples_split=7;, score=(train=0.839, test=0.850) total time=   0.7s\n",
      "[CV 3/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=2, random_forest__min_samples_split=7;, score=(train=0.839, test=0.844) total time=   0.6s\n",
      "[CV 4/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=2, random_forest__min_samples_split=7;, score=(train=0.848, test=0.794) total time=   1.0s\n",
      "[CV 5/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=3, random_forest__min_samples_leaf=2, random_forest__min_samples_split=7;, score=(train=0.847, test=0.825) total time=   1.1s\n",
      "[CV 1/5] END fselection__k=9, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=4, random_forest__min_samples_split=7;, score=(train=0.925, test=0.812) total time=   0.6s\n",
      "[CV 2/5] END fselection__k=9, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=4, random_forest__min_samples_split=7;, score=(train=0.916, test=0.881) total time=   0.3s\n",
      "[CV 3/5] END fselection__k=9, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=4, random_forest__min_samples_split=7;, score=(train=0.906, test=0.856) total time=   0.2s\n",
      "[CV 4/5] END fselection__k=9, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=4, random_forest__min_samples_split=7;, score=(train=0.927, test=0.850) total time=   0.2s\n",
      "[CV 5/5] END fselection__k=9, poly_features__degree=1, random_forest__max_depth=6, random_forest__min_samples_leaf=4, random_forest__min_samples_split=7;, score=(train=0.900, test=0.875) total time=   0.2s\n",
      "[CV 1/5] END fselection__k=10, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=1, random_forest__min_samples_split=7;, score=(train=0.923, test=0.831) total time=   1.5s\n",
      "[CV 2/5] END fselection__k=10, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=1, random_forest__min_samples_split=7;, score=(train=0.916, test=0.838) total time=   2.9s\n",
      "[CV 3/5] END fselection__k=10, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=1, random_forest__min_samples_split=7;, score=(train=0.909, test=0.875) total time=   1.7s\n",
      "[CV 4/5] END fselection__k=10, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=1, random_forest__min_samples_split=7;, score=(train=0.906, test=0.800) total time=   1.1s\n",
      "[CV 5/5] END fselection__k=10, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=1, random_forest__min_samples_split=7;, score=(train=0.898, test=0.838) total time=   1.2s\n",
      "[CV 1/5] END fselection__k=2, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=4, random_forest__min_samples_split=2;, score=(train=0.877, test=0.750) total time=   0.2s\n",
      "[CV 2/5] END fselection__k=2, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=4, random_forest__min_samples_split=2;, score=(train=0.873, test=0.787) total time=   0.2s\n",
      "[CV 3/5] END fselection__k=2, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=4, random_forest__min_samples_split=2;, score=(train=0.852, test=0.831) total time=   0.4s\n",
      "[CV 4/5] END fselection__k=2, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=4, random_forest__min_samples_split=2;, score=(train=0.867, test=0.794) total time=   0.3s\n",
      "[CV 5/5] END fselection__k=2, poly_features__degree=1, random_forest__max_depth=7, random_forest__min_samples_leaf=4, random_forest__min_samples_split=2;, score=(train=0.870, test=0.787) total time=   0.2s\n",
      "[CV 1/5] END fselection__k=9, poly_features__degree=1, random_forest__max_depth=5, random_forest__min_samples_leaf=4, random_forest__min_samples_split=7;, score=(train=0.897, test=0.831) total time=   0.2s\n",
      "[CV 2/5] END fselection__k=9, poly_features__degree=1, random_forest__max_depth=5, random_forest__min_samples_leaf=4, random_forest__min_samples_split=7;, score=(train=0.894, test=0.875) total time=   0.2s\n",
      "[CV 3/5] END fselection__k=9, poly_features__degree=1, random_forest__max_depth=5, random_forest__min_samples_leaf=4, random_forest__min_samples_split=7;, score=(train=0.909, test=0.875) total time=   0.2s\n",
      "[CV 4/5] END fselection__k=9, poly_features__degree=1, random_forest__max_depth=5, random_forest__min_samples_leaf=4, random_forest__min_samples_split=7;, score=(train=0.892, test=0.806) total time=   0.2s\n",
      "[CV 5/5] END fselection__k=9, poly_features__degree=1, random_forest__max_depth=5, random_forest__min_samples_leaf=4, random_forest__min_samples_split=7;, score=(train=0.895, test=0.863) total time=   0.2s\n",
      "[CV 1/5] END fselection__k=8, poly_features__degree=2, random_forest__max_depth=8, random_forest__min_samples_leaf=1, random_forest__min_samples_split=5;, score=(train=0.959, test=0.800) total time=   1.4s\n",
      "[CV 2/5] END fselection__k=8, poly_features__degree=2, random_forest__max_depth=8, random_forest__min_samples_leaf=1, random_forest__min_samples_split=5;, score=(train=0.955, test=0.831) total time=   1.4s\n",
      "[CV 3/5] END fselection__k=8, poly_features__degree=2, random_forest__max_depth=8, random_forest__min_samples_leaf=1, random_forest__min_samples_split=5;, score=(train=0.947, test=0.887) total time=   2.1s\n",
      "[CV 4/5] END fselection__k=8, poly_features__degree=2, random_forest__max_depth=8, random_forest__min_samples_leaf=1, random_forest__min_samples_split=5;, score=(train=0.931, test=0.819) total time=   2.0s\n",
      "[CV 5/5] END fselection__k=8, poly_features__degree=2, random_forest__max_depth=8, random_forest__min_samples_leaf=1, random_forest__min_samples_split=5;, score=(train=0.930, test=0.806) total time=   2.3s\n",
      "[CV 1/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=2, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.778, test=0.769) total time=   1.3s\n",
      "[CV 2/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=2, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.780, test=0.812) total time=   1.7s\n",
      "[CV 3/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=2, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.791, test=0.800) total time=   2.2s\n",
      "[CV 4/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=2, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.797, test=0.744) total time=   2.7s\n",
      "[CV 5/5] END fselection__k=2, poly_features__degree=2, random_forest__max_depth=2, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.781, test=0.769) total time=   1.5s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/5] END fselection__k=5, poly_features__degree=1, random_forest__max_depth=1, random_forest__min_samples_leaf=3, random_forest__min_samples_split=8;, score=(train=0.786, test=0.794) total time=   0.2s\n",
      "[CV 2/5] END fselection__k=5, poly_features__degree=1, random_forest__max_depth=1, random_forest__min_samples_leaf=3, random_forest__min_samples_split=8;, score=(train=0.789, test=0.831) total time=   0.2s\n",
      "[CV 3/5] END fselection__k=5, poly_features__degree=1, random_forest__max_depth=1, random_forest__min_samples_leaf=3, random_forest__min_samples_split=8;, score=(train=0.780, test=0.794) total time=   0.2s\n",
      "[CV 4/5] END fselection__k=5, poly_features__degree=1, random_forest__max_depth=1, random_forest__min_samples_leaf=3, random_forest__min_samples_split=8;, score=(train=0.803, test=0.762) total time=   0.4s\n",
      "[CV 5/5] END fselection__k=5, poly_features__degree=1, random_forest__max_depth=1, random_forest__min_samples_leaf=3, random_forest__min_samples_split=8;, score=(train=0.780, test=0.781) total time=   0.5s\n",
      "[CV 1/5] END fselection__k=10, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.928, test=0.825) total time=   2.5s\n",
      "[CV 2/5] END fselection__k=10, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.923, test=0.825) total time=   1.9s\n",
      "[CV 3/5] END fselection__k=10, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.912, test=0.881) total time=   1.8s\n",
      "[CV 4/5] END fselection__k=10, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.911, test=0.794) total time=   1.1s\n",
      "[CV 5/5] END fselection__k=10, poly_features__degree=2, random_forest__max_depth=6, random_forest__min_samples_leaf=1, random_forest__min_samples_split=6;, score=(train=0.900, test=0.831) total time=   2.3s\n",
      "[CV 1/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=1, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.783, test=0.781) total time=   1.5s\n",
      "[CV 2/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=1, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.784, test=0.812) total time=   2.2s\n",
      "[CV 3/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=1, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.787, test=0.794) total time=   1.5s\n",
      "[CV 4/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=1, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.787, test=0.738) total time=   2.0s\n",
      "[CV 5/5] END fselection__k=6, poly_features__degree=2, random_forest__max_depth=1, random_forest__min_samples_leaf=2, random_forest__min_samples_split=4;, score=(train=0.786, test=0.787) total time=   2.3s\n",
      "[CV 1/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=8, random_forest__min_samples_leaf=3, random_forest__min_samples_split=7;, score=(train=0.923, test=0.850) total time=   0.4s\n",
      "[CV 2/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=8, random_forest__min_samples_leaf=3, random_forest__min_samples_split=7;, score=(train=0.955, test=0.863) total time=   0.5s\n",
      "[CV 3/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=8, random_forest__min_samples_leaf=3, random_forest__min_samples_split=7;, score=(train=0.944, test=0.869) total time=   0.4s\n",
      "[CV 4/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=8, random_forest__min_samples_leaf=3, random_forest__min_samples_split=7;, score=(train=0.944, test=0.819) total time=   0.4s\n",
      "[CV 5/5] END fselection__k=6, poly_features__degree=1, random_forest__max_depth=8, random_forest__min_samples_leaf=3, random_forest__min_samples_split=7;, score=(train=0.961, test=0.869) total time=   0.3s\n"
>>>>>>> e1d1184ffa289bd276b5bba148c1705384e3ee50
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5,\n",
       "                   estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                                             ('poly_features',\n",
       "                                              PolynomialFeatures()),\n",
       "                                             ('fselection',\n",
       "                                              SelectKBest(score_func=<function mutual_info_classif at 0x000002684256B9D0>)),\n",
       "                                             ('random_forest',\n",
       "                                              RandomForestClassifier(max_features='sqrt',\n",
       "                                                                     random_state=18))]),\n",
<<<<<<< HEAD
       "                   n_iter=1,\n",
       "                   param_distributions={'fselection__k': <scipy...\n",
       "                                        'random_forest__max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x0000026845671040>,\n",
       "                                        'random_forest__min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000002684561F1F0>,\n",
       "                                        'random_forest__min_samples_split': <scipy.stats._distn_infrastructure.rv_frozen object at 0x00000268456717C0>},\n",
=======
       "                   n_iter=100,\n",
       "                   param_distributions={'fselection__k': <scipy.stats._di...\n",
       "                                        'random_forest__max_depth': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000002303C4736D0>,\n",
       "                                        'random_forest__min_samples_leaf': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000002303C42A1C0>,\n",
       "                                        'random_forest__min_samples_split': <scipy.stats._distn_infrastructure.rv_frozen object at 0x000002303C473A30>},\n",
>>>>>>> e1d1184ffa289bd276b5bba148c1705384e3ee50
       "                   random_state=18, return_train_score=True, scoring='accuracy',\n",
       "                   verbose=3)"
      ]
     },
<<<<<<< HEAD
     "execution_count": 24,
=======
     "execution_count": 62,
>>>>>>> e1d1184ffa289bd276b5bba148c1705384e3ee50
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = Pipeline([('scaler', StandardScaler()), \n",
    "                ('poly_features', PolynomialFeatures()),\n",
    "                ('fselection', SelectKBest(mutual_info_classif)),\n",
    "                ('random_forest', RandomForestClassifier(n_estimators = 100, random_state = 18, max_features = 'sqrt'))])\n",
    "\n",
    "parameters = {'poly_features__degree':randint(1,3),\n",
    "              'fselection__k':randint(1,11),\n",
    "              'random_forest__max_depth':randint(1,10),\n",
    "              'random_forest__min_samples_leaf':randint(1,5),\n",
    "              'random_forest__min_samples_split':randint(2,10)}\n",
    "\n",
    "rand_clf = RandomizedSearchCV(rfc, \n",
    "                              parameters, \n",
    "                              n_iter = 100, \n",
    "                              random_state = 18, \n",
    "                              cv = 5, \n",
    "                              verbose = 3,\n",
    "                              scoring = 'accuracy', \n",
    "                              return_train_score = True)\n",
    "rand_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 25,
=======
   "execution_count": 63,
>>>>>>> e1d1184ffa289bd276b5bba148c1705384e3ee50
   "id": "c6809e34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'fselection__k': 8, 'poly_features__degree': 1, 'random_forest__max_depth': 7, 'random_forest__min_samples_leaf': 2, 'random_forest__min_samples_split': 5}\n",
      "CV score: 0.87375\n",
      "The test data performance for the best rbf svm through randomized search is: 84.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.8105    0.8462    0.8280        91\n",
      "           1     0.8667    0.8349    0.8505       109\n",
      "\n",
      "    accuracy                         0.8400       200\n",
      "   macro avg     0.8386    0.8405    0.8392       200\n",
      "weighted avg     0.8411    0.8400    0.8402       200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(rand_clf.best_params_) # best paramters\n",
    "print(f'CV score: {rand_clf.best_score_}')\n",
    "\n",
    "# Test\n",
    "y_test_rdCV = rand_clf.predict(X_test)\n",
    "acc_rbf_svm_rd = accuracy_score(y_test, y_test_rdCV)\n",
    "print(f\"The test data performance for the best rbf svm through randomized search is: {acc_rbf_svm_rd:.2%}\")\n",
    "print(classification_report(y_test, y_test_rdCV, digits = 4))"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 26,
=======
   "execution_count": 64,
>>>>>>> e1d1184ffa289bd276b5bba148c1705384e3ee50
   "id": "2871b9c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.038004  , 0.02341305, 0.02193751, 0.04000192,\n",
       "       0.24632101, 0.24879008, 0.38153244])"
      ]
     },
<<<<<<< HEAD
     "execution_count": 26,
=======
     "execution_count": 64,
>>>>>>> e1d1184ffa289bd276b5bba148c1705384e3ee50
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rand_clf.best_estimator_.steps[3][1].feature_importances_\n"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 27,
=======
   "execution_count": 65,
>>>>>>> e1d1184ffa289bd276b5bba148c1705384e3ee50
   "id": "16d65f68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>rf_imp_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>house_area</td>\n",
       "      <td>0.381532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>parent_salary</td>\n",
       "      <td>0.248790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>parent_age</td>\n",
       "      <td>0.246321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>residence</td>\n",
       "      <td>0.040002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>school_accreditation</td>\n",
       "      <td>0.038004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>gender</td>\n",
       "      <td>0.023413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>interest</td>\n",
       "      <td>0.021938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>type_school</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                feature  rf_imp_score\n",
       "7            house_area      0.381532\n",
       "6         parent_salary      0.248790\n",
       "5            parent_age      0.246321\n",
       "4             residence      0.040002\n",
       "1  school_accreditation      0.038004\n",
       "2                gender      0.023413\n",
       "3              interest      0.021938\n",
       "0           type_school      0.000000"
      ]
     },
<<<<<<< HEAD
     "execution_count": 27,
=======
     "execution_count": 65,
>>>>>>> e1d1184ffa289bd276b5bba148c1705384e3ee50
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg_imp = pd.DataFrame(list(zip(X.columns, rand_clf.best_estimator_.steps[3][1].feature_importances_)), \n",
    "                     columns = ['feature', 'rf_imp_score']).sort_values(by = 'rf_imp_score', ascending = False)\n",
    "xg_imp"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 28,
=======
   "execution_count": 66,
>>>>>>> e1d1184ffa289bd276b5bba148c1705384e3ee50
   "id": "d8edeeda",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "image/png": 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",
=======
      "image/png": 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\n",
>>>>>>> e1d1184ffa289bd276b5bba148c1705384e3ee50
      "text/plain": [
       "<Figure size 792x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(11,5)})\n",
    "ax = sns.barplot(x = 'feature', y = 'rf_imp_score', data = xg_imp)\n",
    "ax.set_title('XGBoost Feature Importance for Wine Quality')\n",
    "ax.set(ylabel='Feature importance')\n",
    "ax.set(xlabel='Features')\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation = 30); # rotate x label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e251e323",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.885"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#{'fselection__k': 15, 'poly_features__degree': 1, 'random_forest__max_depth': 8, \n",
    "#'random_forest__min_samples_leaf': 1, 'random_forest__min_samples_split': 7}\n",
    "\n",
    "clf = RandomForestClassifier(n_estimators = 100, random_state = 18, \n",
    "                             max_features = 'sqrt',max_depth = 8, min_samples_leaf = 1, min_samples_split = 7)\n",
    "\n",
    "clf.fit(X_train, y_train)\n",
    "y_test_sk = clf.predict(X_test)\n",
    "acc_sk = accuracy_score(y_test, y_test_sk)\n",
    "acc_sk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d6b085b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f_i = list(zip(X.columns,clf.feature_importances_))\n",
    "f_i.sort(key = lambda x : x[1])\n",
    "plt.barh([x[0] for x in f_i],[x[1] for x in f_i])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc3654f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dae847b8",
   "metadata": {},
   "source": [
    "The Random forest model divides the methods based on how 'pure' each of the features are. Using the gini coefficient, each features (ex. average_grades) is listed on the tree on the order of least to greatest impurity. The lower the impurity means that a feature is able to distinguish the outcome variables better than other features. Therefore, lower impurity means that it is a better predictor and a more important feature in comparison to the other features. \n",
    "\n",
    "After removing the variables with the lowest feature importance, the model went from 86.5 to 88.5% "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b1c8868",
   "metadata": {},
   "source": [
    "## LOG REG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "fa1c9af7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "85233db3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 150 candidates, totalling 750 fits\n",
      "[CV 1/5] END fselection__k=1, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.511, test=0.512) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=1, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.511, test=0.512) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=1, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.511, test=0.512) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=1, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.511, test=0.512) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=1, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.512, test=0.506) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=1, logistic_regression__C=0.01, poly_features__degree=2;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=1, logistic_regression__C=0.01, poly_features__degree=2;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=1, logistic_regression__C=0.01, poly_features__degree=2;, score=(train=0.592, test=0.613) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=1, logistic_regression__C=0.01, poly_features__degree=2;, score=(train=0.600, test=0.581) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=1, logistic_regression__C=0.01, poly_features__degree=2;, score=(train=0.602, test=0.575) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=1, logistic_regression__C=0.01, poly_features__degree=3;, score=(train=0.584, test=0.600) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=1, logistic_regression__C=0.01, poly_features__degree=3;, score=(train=0.586, test=0.594) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=1, logistic_regression__C=0.01, poly_features__degree=3;, score=(train=0.586, test=0.594) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=1, logistic_regression__C=0.01, poly_features__degree=3;, score=(train=0.592, test=0.569) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=1, logistic_regression__C=0.01, poly_features__degree=3;, score=(train=0.589, test=0.581) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=1, logistic_regression__C=0.1, poly_features__degree=1;, score=(train=0.547, test=0.537) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=1, logistic_regression__C=0.1, poly_features__degree=1;, score=(train=0.550, test=0.525) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=1, logistic_regression__C=0.1, poly_features__degree=1;, score=(train=0.534, test=0.588) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=1, logistic_regression__C=0.1, poly_features__degree=1;, score=(train=0.550, test=0.525) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=1, logistic_regression__C=0.1, poly_features__degree=1;, score=(train=0.544, test=0.550) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=1, logistic_regression__C=0.1, poly_features__degree=2;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=1, logistic_regression__C=0.1, poly_features__degree=2;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=1, logistic_regression__C=0.1, poly_features__degree=2;, score=(train=0.592, test=0.613) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=1, logistic_regression__C=0.1, poly_features__degree=2;, score=(train=0.600, test=0.581) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=1, logistic_regression__C=0.1, poly_features__degree=2;, score=(train=0.602, test=0.575) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=1, logistic_regression__C=0.1, poly_features__degree=3;, score=(train=0.584, test=0.600) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=1, logistic_regression__C=0.1, poly_features__degree=3;, score=(train=0.586, test=0.594) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=1, logistic_regression__C=0.1, poly_features__degree=3;, score=(train=0.586, test=0.594) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=1, logistic_regression__C=0.1, poly_features__degree=3;, score=(train=0.592, test=0.569) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=1, logistic_regression__C=0.1, poly_features__degree=3;, score=(train=0.589, test=0.581) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=1, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.547, test=0.537) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=1, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.550, test=0.525) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=1, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.534, test=0.588) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=1, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.550, test=0.525) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=1, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.544, test=0.550) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=1, logistic_regression__C=1, poly_features__degree=2;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=1, logistic_regression__C=1, poly_features__degree=2;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=1, logistic_regression__C=1, poly_features__degree=2;, score=(train=0.592, test=0.613) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=1, logistic_regression__C=1, poly_features__degree=2;, score=(train=0.600, test=0.581) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=1, logistic_regression__C=1, poly_features__degree=2;, score=(train=0.602, test=0.575) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=1, logistic_regression__C=1, poly_features__degree=3;, score=(train=0.584, test=0.600) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=1, logistic_regression__C=1, poly_features__degree=3;, score=(train=0.586, test=0.594) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=1, logistic_regression__C=1, poly_features__degree=3;, score=(train=0.586, test=0.594) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=1, logistic_regression__C=1, poly_features__degree=3;, score=(train=0.592, test=0.569) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=1, logistic_regression__C=1, poly_features__degree=3;, score=(train=0.589, test=0.581) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=1, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.547, test=0.537) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=1, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.550, test=0.525) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=1, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.534, test=0.588) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=1, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.550, test=0.525) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=1, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.544, test=0.550) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=1, logistic_regression__C=10, poly_features__degree=2;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=1, logistic_regression__C=10, poly_features__degree=2;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=1, logistic_regression__C=10, poly_features__degree=2;, score=(train=0.592, test=0.613) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=1, logistic_regression__C=10, poly_features__degree=2;, score=(train=0.600, test=0.581) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=1, logistic_regression__C=10, poly_features__degree=2;, score=(train=0.602, test=0.575) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=1, logistic_regression__C=10, poly_features__degree=3;, score=(train=0.584, test=0.600) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=1, logistic_regression__C=10, poly_features__degree=3;, score=(train=0.586, test=0.594) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=1, logistic_regression__C=10, poly_features__degree=3;, score=(train=0.586, test=0.594) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=1, logistic_regression__C=10, poly_features__degree=3;, score=(train=0.592, test=0.569) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=1, logistic_regression__C=10, poly_features__degree=3;, score=(train=0.589, test=0.581) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=1, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.547, test=0.537) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=1, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.550, test=0.525) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=1, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.534, test=0.588) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=1, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.550, test=0.525) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=1, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.544, test=0.550) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=1, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=1, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=1, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.592, test=0.613) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=1, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.600, test=0.581) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=1, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.602, test=0.575) total time=   0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/5] END fselection__k=1, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.584, test=0.600) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=1, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.586, test=0.594) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=1, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.586, test=0.594) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=1, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.592, test=0.569) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=1, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.589, test=0.581) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=2, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.616, test=0.600) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=2, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.570, test=0.556) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=2, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.597, test=0.675) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=2, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.619, test=0.588) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=2, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.620, test=0.581) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=2, logistic_regression__C=0.01, poly_features__degree=2;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=2, logistic_regression__C=0.01, poly_features__degree=2;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=2, logistic_regression__C=0.01, poly_features__degree=2;, score=(train=0.592, test=0.613) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=2, logistic_regression__C=0.01, poly_features__degree=2;, score=(train=0.600, test=0.581) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=2, logistic_regression__C=0.01, poly_features__degree=2;, score=(train=0.602, test=0.575) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=2, logistic_regression__C=0.01, poly_features__degree=3;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=2, logistic_regression__C=0.01, poly_features__degree=3;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=2, logistic_regression__C=0.01, poly_features__degree=3;, score=(train=0.592, test=0.613) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=2, logistic_regression__C=0.01, poly_features__degree=3;, score=(train=0.600, test=0.581) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=2, logistic_regression__C=0.01, poly_features__degree=3;, score=(train=0.602, test=0.575) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=2, logistic_regression__C=0.1, poly_features__degree=1;, score=(train=0.616, test=0.600) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=2, logistic_regression__C=0.1, poly_features__degree=1;, score=(train=0.570, test=0.556) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=2, logistic_regression__C=0.1, poly_features__degree=1;, score=(train=0.597, test=0.675) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=2, logistic_regression__C=0.1, poly_features__degree=1;, score=(train=0.619, test=0.588) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=2, logistic_regression__C=0.1, poly_features__degree=1;, score=(train=0.620, test=0.581) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=2, logistic_regression__C=0.1, poly_features__degree=2;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=2, logistic_regression__C=0.1, poly_features__degree=2;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=2, logistic_regression__C=0.1, poly_features__degree=2;, score=(train=0.592, test=0.613) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=2, logistic_regression__C=0.1, poly_features__degree=2;, score=(train=0.600, test=0.581) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=2, logistic_regression__C=0.1, poly_features__degree=2;, score=(train=0.602, test=0.575) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=2, logistic_regression__C=0.1, poly_features__degree=3;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=2, logistic_regression__C=0.1, poly_features__degree=3;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=2, logistic_regression__C=0.1, poly_features__degree=3;, score=(train=0.592, test=0.613) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=2, logistic_regression__C=0.1, poly_features__degree=3;, score=(train=0.600, test=0.581) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=2, logistic_regression__C=0.1, poly_features__degree=3;, score=(train=0.602, test=0.575) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=2, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.616, test=0.600) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=2, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.570, test=0.556) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=2, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.597, test=0.675) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=2, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.619, test=0.588) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=2, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.620, test=0.581) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=2, logistic_regression__C=1, poly_features__degree=2;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=2, logistic_regression__C=1, poly_features__degree=2;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=2, logistic_regression__C=1, poly_features__degree=2;, score=(train=0.592, test=0.613) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=2, logistic_regression__C=1, poly_features__degree=2;, score=(train=0.600, test=0.581) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=2, logistic_regression__C=1, poly_features__degree=2;, score=(train=0.602, test=0.575) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=2, logistic_regression__C=1, poly_features__degree=3;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=2, logistic_regression__C=1, poly_features__degree=3;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=2, logistic_regression__C=1, poly_features__degree=3;, score=(train=0.592, test=0.613) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=2, logistic_regression__C=1, poly_features__degree=3;, score=(train=0.600, test=0.581) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=2, logistic_regression__C=1, poly_features__degree=3;, score=(train=0.602, test=0.575) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=2, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.616, test=0.600) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=2, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.570, test=0.556) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=2, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.597, test=0.675) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=2, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.619, test=0.588) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=2, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.620, test=0.581) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=2, logistic_regression__C=10, poly_features__degree=2;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=2, logistic_regression__C=10, poly_features__degree=2;, score=(train=0.594, test=0.606) total time=   0.0s\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 3/5] END fselection__k=2, logistic_regression__C=10, poly_features__degree=3;, score=(train=0.592, test=0.613) total time=   0.0s\n",
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      "[CV 2/5] END fselection__k=3, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.611, test=0.619) total time=   0.0s\n",
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      "[CV 4/5] END fselection__k=3, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.619, test=0.588) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=3, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.736, test=0.719) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=3, logistic_regression__C=1, poly_features__degree=2;, score=(train=0.633, test=0.581) total time=   0.0s\n",
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      "[CV 5/5] END fselection__k=3, logistic_regression__C=1, poly_features__degree=2;, score=(train=0.780, test=0.731) total time=   0.0s\n",
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      "[CV 3/5] END fselection__k=3, logistic_regression__C=1, poly_features__degree=3;, score=(train=0.592, test=0.613) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=3, logistic_regression__C=1, poly_features__degree=3;, score=(train=0.600, test=0.581) total time=   0.0s\n"
     ]
    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 5/5] END fselection__k=3, logistic_regression__C=1, poly_features__degree=3;, score=(train=0.602, test=0.575) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=3, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.616, test=0.600) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=3, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.611, test=0.619) total time=   0.0s\n",
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      "[CV 4/5] END fselection__k=3, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.619, test=0.588) total time=   0.0s\n",
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      "[CV 1/5] END fselection__k=3, logistic_regression__C=10, poly_features__degree=2;, score=(train=0.633, test=0.581) total time=   0.0s\n",
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      "[CV 3/5] END fselection__k=3, logistic_regression__C=10, poly_features__degree=2;, score=(train=0.617, test=0.644) total time=   0.0s\n",
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      "[CV 5/5] END fselection__k=3, logistic_regression__C=10, poly_features__degree=2;, score=(train=0.777, test=0.719) total time=   0.0s\n",
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      "[CV 2/5] END fselection__k=3, logistic_regression__C=10, poly_features__degree=3;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=3, logistic_regression__C=10, poly_features__degree=3;, score=(train=0.592, test=0.613) total time=   0.0s\n",
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      "[CV 1/5] END fselection__k=3, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.616, test=0.600) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=3, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.611, test=0.619) total time=   0.0s\n",
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      "[CV 4/5] END fselection__k=3, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.619, test=0.588) total time=   0.0s\n",
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      "[CV 1/5] END fselection__k=3, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.633, test=0.581) total time=   0.0s\n",
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      "[CV 3/5] END fselection__k=3, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.617, test=0.644) total time=   0.0s\n",
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      "[CV 5/5] END fselection__k=3, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.775, test=0.719) total time=   0.0s\n",
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      "[CV 4/5] END fselection__k=3, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.600, test=0.581) total time=   0.0s\n",
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      "[CV 1/5] END fselection__k=4, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.656, test=0.644) total time=   0.0s\n",
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      "[CV 1/5] END fselection__k=4, logistic_regression__C=0.1, poly_features__degree=1;, score=(train=0.698, test=0.694) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=4, logistic_regression__C=0.1, poly_features__degree=1;, score=(train=0.675, test=0.700) total time=   0.0s\n",
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      "[CV 1/5] END fselection__k=4, logistic_regression__C=0.1, poly_features__degree=2;, score=(train=0.644, test=0.625) total time=   0.0s\n",
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      "[CV 3/5] END fselection__k=4, logistic_regression__C=0.1, poly_features__degree=2;, score=(train=0.628, test=0.688) total time=   0.0s\n",
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      "[CV 5/5] END fselection__k=4, logistic_regression__C=0.1, poly_features__degree=2;, score=(train=0.744, test=0.706) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=4, logistic_regression__C=0.1, poly_features__degree=3;, score=(train=0.594, test=0.606) total time=   0.0s\n",
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      "[CV 3/5] END fselection__k=4, logistic_regression__C=0.1, poly_features__degree=3;, score=(train=0.592, test=0.613) total time=   0.0s\n",
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      "[CV 5/5] END fselection__k=4, logistic_regression__C=0.1, poly_features__degree=3;, score=(train=0.602, test=0.575) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=4, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.713, test=0.719) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=4, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.719, test=0.769) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=4, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.717, test=0.806) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=4, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.742, test=0.675) total time=   0.0s\n",
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      "[CV 4/5] END fselection__k=4, logistic_regression__C=1, poly_features__degree=2;, score=(train=0.642, test=0.631) total time=   0.0s\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/5] END fselection__k=4, logistic_regression__C=1, poly_features__degree=3;, score=(train=0.594, test=0.606) total time=   0.0s\n",
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      "[CV 1/5] END fselection__k=4, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.722, test=0.706) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=4, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.734, test=0.769) total time=   0.0s\n",
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      "[CV 2/5] END fselection__k=4, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.641, test=0.637) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=4, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.628, test=0.688) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=4, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.642, test=0.631) total time=   0.0s\n",
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      "[CV 1/5] END fselection__k=4, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=4, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=4, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.592, test=0.613) total time=   0.1s\n",
      "[CV 4/5] END fselection__k=4, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.600, test=0.581) total time=   0.0s\n",
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      "[CV 1/5] END fselection__k=5, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.694, test=0.681) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=5, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.683, test=0.644) total time=   0.0s\n",
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      "[CV 5/5] END fselection__k=5, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.694, test=0.662) total time=   0.0s\n",
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      "[CV 5/5] END fselection__k=5, logistic_regression__C=0.01, poly_features__degree=2;, score=(train=0.736, test=0.681) total time=   0.0s\n",
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      "[CV 2/5] END fselection__k=5, logistic_regression__C=0.01, poly_features__degree=3;, score=(train=0.594, test=0.606) total time=   0.0s\n",
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      "[CV 1/5] END fselection__k=5, logistic_regression__C=0.1, poly_features__degree=1;, score=(train=0.733, test=0.738) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=5, logistic_regression__C=0.1, poly_features__degree=1;, score=(train=0.727, test=0.731) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=5, logistic_regression__C=0.1, poly_features__degree=1;, score=(train=0.723, test=0.781) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=5, logistic_regression__C=0.1, poly_features__degree=1;, score=(train=0.706, test=0.650) total time=   0.0s\n",
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      "[CV 1/5] END fselection__k=5, logistic_regression__C=0.1, poly_features__degree=2;, score=(train=0.642, test=0.613) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=5, logistic_regression__C=0.1, poly_features__degree=2;, score=(train=0.637, test=0.631) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=5, logistic_regression__C=0.1, poly_features__degree=2;, score=(train=0.689, test=0.719) total time=   0.0s\n",
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      "[CV 1/5] END fselection__k=5, logistic_regression__C=0.1, poly_features__degree=3;, score=(train=0.594, test=0.606) total time=   0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 2/5] END fselection__k=5, logistic_regression__C=0.1, poly_features__degree=3;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=5, logistic_regression__C=0.1, poly_features__degree=3;, score=(train=0.592, test=0.613) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=5, logistic_regression__C=0.1, poly_features__degree=3;, score=(train=0.600, test=0.581) total time=   0.0s\n",
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      "[CV 1/5] END fselection__k=5, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.758, test=0.769) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=5, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.759, test=0.769) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=5, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.752, test=0.831) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=5, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.775, test=0.700) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=5, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.769, test=0.756) total time=   0.0s\n",
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      "[CV 2/5] END fselection__k=5, logistic_regression__C=1, poly_features__degree=2;, score=(train=0.637, test=0.631) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=5, logistic_regression__C=1, poly_features__degree=2;, score=(train=0.689, test=0.719) total time=   0.0s\n",
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      "[CV 5/5] END fselection__k=5, logistic_regression__C=1, poly_features__degree=2;, score=(train=0.766, test=0.713) total time=   0.0s\n",
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      "[CV 2/5] END fselection__k=5, logistic_regression__C=1, poly_features__degree=3;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=5, logistic_regression__C=1, poly_features__degree=3;, score=(train=0.592, test=0.613) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=5, logistic_regression__C=1, poly_features__degree=3;, score=(train=0.600, test=0.581) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=5, logistic_regression__C=1, poly_features__degree=3;, score=(train=0.602, test=0.575) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=5, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.770, test=0.769) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=5, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.761, test=0.781) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=5, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.759, test=0.825) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=5, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.783, test=0.706) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=5, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.773, test=0.762) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=5, logistic_regression__C=10, poly_features__degree=2;, score=(train=0.644, test=0.625) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=5, logistic_regression__C=10, poly_features__degree=2;, score=(train=0.641, test=0.637) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=5, logistic_regression__C=10, poly_features__degree=2;, score=(train=0.689, test=0.719) total time=   0.0s\n",
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      "[CV 1/5] END fselection__k=5, logistic_regression__C=10, poly_features__degree=3;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=5, logistic_regression__C=10, poly_features__degree=3;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=5, logistic_regression__C=10, poly_features__degree=3;, score=(train=0.592, test=0.613) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=5, logistic_regression__C=10, poly_features__degree=3;, score=(train=0.600, test=0.581) total time=   0.0s\n",
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      "[CV 1/5] END fselection__k=5, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.767, test=0.775) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=5, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.764, test=0.775) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=5, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.756, test=0.825) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=5, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.783, test=0.706) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=5, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.769, test=0.762) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=5, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.644, test=0.625) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=5, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.641, test=0.637) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=5, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.689, test=0.719) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=5, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.706, test=0.650) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=5, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.758, test=0.725) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=5, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=5, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=5, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.592, test=0.613) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=5, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.600, test=0.581) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=5, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.602, test=0.575) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=6, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.703, test=0.706) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=6, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.695, test=0.706) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=6, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.694, test=0.731) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=6, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.706, test=0.694) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=6, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.703, test=0.694) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=6, logistic_regression__C=0.01, poly_features__degree=2;, score=(train=0.642, test=0.613) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=6, logistic_regression__C=0.01, poly_features__degree=2;, score=(train=0.637, test=0.631) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=6, logistic_regression__C=0.01, poly_features__degree=2;, score=(train=0.689, test=0.719) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=6, logistic_regression__C=0.01, poly_features__degree=2;, score=(train=0.702, test=0.650) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=6, logistic_regression__C=0.01, poly_features__degree=2;, score=(train=0.745, test=0.700) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=6, logistic_regression__C=0.01, poly_features__degree=3;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=6, logistic_regression__C=0.01, poly_features__degree=3;, score=(train=0.594, test=0.606) total time=   0.0s\n",
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      "[CV 4/5] END fselection__k=6, logistic_regression__C=0.01, poly_features__degree=3;, score=(train=0.600, test=0.581) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=6, logistic_regression__C=0.01, poly_features__degree=3;, score=(train=0.602, test=0.575) total time=   0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 1/5] END fselection__k=6, logistic_regression__C=0.1, poly_features__degree=1;, score=(train=0.770, test=0.787) total time=   0.0s\n",
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      "[CV 1/5] END fselection__k=6, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.809, test=0.806) total time=   0.0s\n",
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      "[CV 4/5] END fselection__k=6, logistic_regression__C=10, poly_features__degree=3;, score=(train=0.608, test=0.581) total time=   0.0s\n",
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      "[CV 1/5] END fselection__k=6, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.811, test=0.800) total time=   0.0s\n",
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      "[CV 3/5] END fselection__k=6, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.797, test=0.875) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=6, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.828, test=0.744) total time=   0.0s\n",
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      "[CV 1/5] END fselection__k=6, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.644, test=0.625) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=6, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.641, test=0.637) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=6, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.689, test=0.719) total time=   0.0s\n",
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      "[CV 5/5] END fselection__k=6, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.759, test=0.725) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=6, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.606, test=0.625) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=6, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=6, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.603, test=0.600) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=6, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.608, test=0.581) total time=   0.0s\n",
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      "[CV 1/5] END fselection__k=7, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.714, test=0.688) total time=   0.0s\n",
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      "[CV 3/5] END fselection__k=7, logistic_regression__C=0.01, poly_features__degree=2;, score=(train=0.689, test=0.719) total time=   0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 4/5] END fselection__k=7, logistic_regression__C=0.01, poly_features__degree=2;, score=(train=0.702, test=0.650) total time=   0.0s\n",
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      "[CV 2/5] END fselection__k=7, logistic_regression__C=0.1, poly_features__degree=1;, score=(train=0.762, test=0.762) total time=   0.0s\n",
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      "[CV 3/5] END fselection__k=7, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.795, test=0.850) total time=   0.0s\n",
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      "[CV 5/5] END fselection__k=7, logistic_regression__C=1, poly_features__degree=2;, score=(train=0.766, test=0.713) total time=   0.0s\n",
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      "[CV 4/5] END fselection__k=7, logistic_regression__C=1, poly_features__degree=3;, score=(train=0.603, test=0.581) total time=   0.0s\n",
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      "[CV 1/5] END fselection__k=7, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.812, test=0.806) total time=   0.0s\n",
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      "[CV 3/5] END fselection__k=7, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.795, test=0.869) total time=   0.0s\n",
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      "[CV 1/5] END fselection__k=7, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.816, test=0.812) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=7, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.806, test=0.812) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=7, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.795, test=0.869) total time=   0.0s\n",
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      "[CV 3/5] END fselection__k=7, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.689, test=0.719) total time=   0.0s\n",
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      "[CV 2/5] END fselection__k=7, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.609, test=0.619) total time=   0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 3/5] END fselection__k=7, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.613, test=0.619) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=7, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.620, test=0.588) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=7, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.613, test=0.594) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=8, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.719, test=0.694) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=8, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.689, test=0.731) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=8, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.686, test=0.756) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=8, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.695, test=0.650) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=8, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.719, test=0.675) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=8, logistic_regression__C=0.01, poly_features__degree=2;, score=(train=0.694, test=0.681) total time=   0.0s\n",
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      "[CV 3/5] END fselection__k=8, logistic_regression__C=0.01, poly_features__degree=2;, score=(train=0.745, test=0.775) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=8, logistic_regression__C=0.01, poly_features__degree=2;, score=(train=0.702, test=0.650) total time=   0.0s\n",
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      "[CV 1/5] END fselection__k=8, logistic_regression__C=0.01, poly_features__degree=3;, score=(train=0.594, test=0.606) total time=   0.0s\n",
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      "[CV 1/5] END fselection__k=8, logistic_regression__C=0.1, poly_features__degree=1;, score=(train=0.766, test=0.781) total time=   0.0s\n",
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      "[CV 3/5] END fselection__k=8, logistic_regression__C=0.1, poly_features__degree=1;, score=(train=0.772, test=0.831) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=8, logistic_regression__C=0.1, poly_features__degree=1;, score=(train=0.766, test=0.694) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=8, logistic_regression__C=0.1, poly_features__degree=1;, score=(train=0.764, test=0.738) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=8, logistic_regression__C=0.1, poly_features__degree=2;, score=(train=0.694, test=0.681) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=8, logistic_regression__C=0.1, poly_features__degree=2;, score=(train=0.637, test=0.631) total time=   0.0s\n",
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      "[CV 4/5] END fselection__k=8, logistic_regression__C=0.1, poly_features__degree=2;, score=(train=0.702, test=0.650) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=8, logistic_regression__C=0.1, poly_features__degree=2;, score=(train=0.738, test=0.688) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=8, logistic_regression__C=0.1, poly_features__degree=3;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=8, logistic_regression__C=0.1, poly_features__degree=3;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=8, logistic_regression__C=0.1, poly_features__degree=3;, score=(train=0.592, test=0.613) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=8, logistic_regression__C=0.1, poly_features__degree=3;, score=(train=0.600, test=0.581) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=8, logistic_regression__C=0.1, poly_features__degree=3;, score=(train=0.602, test=0.575) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=8, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.808, test=0.794) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=8, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.808, test=0.781) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=8, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.794, test=0.869) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=8, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.828, test=0.725) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=8, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.809, test=0.800) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=8, logistic_regression__C=1, poly_features__degree=2;, score=(train=0.695, test=0.694) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=8, logistic_regression__C=1, poly_features__degree=2;, score=(train=0.641, test=0.637) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=8, logistic_regression__C=1, poly_features__degree=2;, score=(train=0.780, test=0.838) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=8, logistic_regression__C=1, poly_features__degree=2;, score=(train=0.706, test=0.650) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=8, logistic_regression__C=1, poly_features__degree=2;, score=(train=0.767, test=0.713) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=8, logistic_regression__C=1, poly_features__degree=3;, score=(train=0.595, test=0.606) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=8, logistic_regression__C=1, poly_features__degree=3;, score=(train=0.594, test=0.619) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=8, logistic_regression__C=1, poly_features__degree=3;, score=(train=0.595, test=0.613) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=8, logistic_regression__C=1, poly_features__degree=3;, score=(train=0.603, test=0.581) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=8, logistic_regression__C=1, poly_features__degree=3;, score=(train=0.602, test=0.575) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=8, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.812, test=0.806) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=8, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.802, test=0.806) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=8, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.792, test=0.875) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=8, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.825, test=0.750) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=8, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.814, test=0.800) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=8, logistic_regression__C=10, poly_features__degree=2;, score=(train=0.694, test=0.694) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=8, logistic_regression__C=10, poly_features__degree=2;, score=(train=0.642, test=0.637) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=8, logistic_regression__C=10, poly_features__degree=2;, score=(train=0.787, test=0.838) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=8, logistic_regression__C=10, poly_features__degree=2;, score=(train=0.705, test=0.650) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=8, logistic_regression__C=10, poly_features__degree=2;, score=(train=0.767, test=0.725) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=8, logistic_regression__C=10, poly_features__degree=3;, score=(train=0.608, test=0.625) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=8, logistic_regression__C=10, poly_features__degree=3;, score=(train=0.609, test=0.619) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=8, logistic_regression__C=10, poly_features__degree=3;, score=(train=0.611, test=0.619) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=8, logistic_regression__C=10, poly_features__degree=3;, score=(train=0.619, test=0.588) total time=   0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 5/5] END fselection__k=8, logistic_regression__C=10, poly_features__degree=3;, score=(train=0.613, test=0.606) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=8, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.816, test=0.812) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=8, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.802, test=0.806) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=8, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.792, test=0.869) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=8, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.827, test=0.750) total time=   0.0s\n",
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      "[CV 1/5] END fselection__k=8, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.694, test=0.694) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=8, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.644, test=0.637) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=8, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.791, test=0.825) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=8, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.705, test=0.650) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=8, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.766, test=0.725) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=8, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.609, test=0.619) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=8, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.611, test=0.619) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=8, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.613, test=0.619) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=8, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.620, test=0.588) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=8, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.614, test=0.606) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=9, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.716, test=0.725) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=9, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.684, test=0.725) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=9, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.677, test=0.756) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=9, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.694, test=0.613) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=9, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.725, test=0.662) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=9, logistic_regression__C=0.01, poly_features__degree=2;, score=(train=0.698, test=0.675) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=9, logistic_regression__C=0.01, poly_features__degree=2;, score=(train=0.686, test=0.706) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=9, logistic_regression__C=0.01, poly_features__degree=2;, score=(train=0.714, test=0.762) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=9, logistic_regression__C=0.01, poly_features__degree=2;, score=(train=0.702, test=0.650) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=9, logistic_regression__C=0.01, poly_features__degree=2;, score=(train=0.748, test=0.694) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=9, logistic_regression__C=0.01, poly_features__degree=3;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=9, logistic_regression__C=0.01, poly_features__degree=3;, score=(train=0.594, test=0.606) total time=   0.0s\n",
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      "[CV 4/5] END fselection__k=9, logistic_regression__C=0.01, poly_features__degree=3;, score=(train=0.600, test=0.581) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=9, logistic_regression__C=0.01, poly_features__degree=3;, score=(train=0.602, test=0.575) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=9, logistic_regression__C=0.1, poly_features__degree=1;, score=(train=0.775, test=0.781) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=9, logistic_regression__C=0.1, poly_features__degree=1;, score=(train=0.764, test=0.756) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=9, logistic_regression__C=0.1, poly_features__degree=1;, score=(train=0.773, test=0.825) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=9, logistic_regression__C=0.1, poly_features__degree=1;, score=(train=0.767, test=0.694) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=9, logistic_regression__C=0.1, poly_features__degree=1;, score=(train=0.764, test=0.762) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=9, logistic_regression__C=0.1, poly_features__degree=2;, score=(train=0.717, test=0.713) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=9, logistic_regression__C=0.1, poly_features__degree=2;, score=(train=0.689, test=0.713) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=9, logistic_regression__C=0.1, poly_features__degree=2;, score=(train=0.719, test=0.775) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=9, logistic_regression__C=0.1, poly_features__degree=2;, score=(train=0.702, test=0.650) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=9, logistic_regression__C=0.1, poly_features__degree=2;, score=(train=0.755, test=0.694) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=9, logistic_regression__C=0.1, poly_features__degree=3;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=9, logistic_regression__C=0.1, poly_features__degree=3;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=9, logistic_regression__C=0.1, poly_features__degree=3;, score=(train=0.592, test=0.613) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=9, logistic_regression__C=0.1, poly_features__degree=3;, score=(train=0.600, test=0.581) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=9, logistic_regression__C=0.1, poly_features__degree=3;, score=(train=0.602, test=0.575) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=9, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.812, test=0.794) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=9, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.808, test=0.787) total time=   0.0s\n",
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      "[CV 4/5] END fselection__k=9, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.823, test=0.738) total time=   0.0s\n",
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      "[CV 1/5] END fselection__k=9, logistic_regression__C=1, poly_features__degree=2;, score=(train=0.773, test=0.738) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=9, logistic_regression__C=1, poly_features__degree=2;, score=(train=0.741, test=0.787) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=9, logistic_regression__C=1, poly_features__degree=2;, score=(train=0.780, test=0.844) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=9, logistic_regression__C=1, poly_features__degree=2;, score=(train=0.706, test=0.650) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=9, logistic_regression__C=1, poly_features__degree=2;, score=(train=0.773, test=0.725) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=9, logistic_regression__C=1, poly_features__degree=3;, score=(train=0.595, test=0.606) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=9, logistic_regression__C=1, poly_features__degree=3;, score=(train=0.594, test=0.619) total time=   0.0s\n",
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      "[CV 4/5] END fselection__k=9, logistic_regression__C=1, poly_features__degree=3;, score=(train=0.605, test=0.581) total time=   0.0s\n",
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      "[CV 1/5] END fselection__k=9, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.816, test=0.787) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=9, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.802, test=0.806) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=9, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.792, test=0.875) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=9, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.822, test=0.750) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=9, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.820, test=0.806) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=9, logistic_regression__C=10, poly_features__degree=2;, score=(train=0.805, test=0.725) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=9, logistic_regression__C=10, poly_features__degree=2;, score=(train=0.741, test=0.787) total time=   0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 3/5] END fselection__k=9, logistic_regression__C=10, poly_features__degree=2;, score=(train=0.794, test=0.825) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=9, logistic_regression__C=10, poly_features__degree=2;, score=(train=0.706, test=0.656) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=9, logistic_regression__C=10, poly_features__degree=2;, score=(train=0.797, test=0.725) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=9, logistic_regression__C=10, poly_features__degree=3;, score=(train=0.608, test=0.625) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=9, logistic_regression__C=10, poly_features__degree=3;, score=(train=0.609, test=0.619) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=9, logistic_regression__C=10, poly_features__degree=3;, score=(train=0.611, test=0.619) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=9, logistic_regression__C=10, poly_features__degree=3;, score=(train=0.619, test=0.588) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=9, logistic_regression__C=10, poly_features__degree=3;, score=(train=0.613, test=0.606) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=9, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.814, test=0.781) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=9, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.802, test=0.806) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=9, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.792, test=0.869) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=9, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.820, test=0.750) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=9, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.817, test=0.800) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=9, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.806, test=0.719) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=9, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.741, test=0.787) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=9, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.791, test=0.825) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=9, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.705, test=0.656) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=9, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.791, test=0.725) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=9, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.609, test=0.619) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=9, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.611, test=0.619) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=9, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.613, test=0.619) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=9, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.620, test=0.588) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=9, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.616, test=0.606) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=10, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.709, test=0.719) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=10, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.686, test=0.713) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=10, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.692, test=0.762) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=10, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.680, test=0.631) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=10, logistic_regression__C=0.01, poly_features__degree=1;, score=(train=0.722, test=0.675) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=10, logistic_regression__C=0.01, poly_features__degree=2;, score=(train=0.758, test=0.731) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=10, logistic_regression__C=0.01, poly_features__degree=2;, score=(train=0.666, test=0.700) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=10, logistic_regression__C=0.01, poly_features__degree=2;, score=(train=0.708, test=0.750) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=10, logistic_regression__C=0.01, poly_features__degree=2;, score=(train=0.695, test=0.650) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=10, logistic_regression__C=0.01, poly_features__degree=2;, score=(train=0.766, test=0.738) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=10, logistic_regression__C=0.01, poly_features__degree=3;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=10, logistic_regression__C=0.01, poly_features__degree=3;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=10, logistic_regression__C=0.01, poly_features__degree=3;, score=(train=0.592, test=0.613) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=10, logistic_regression__C=0.01, poly_features__degree=3;, score=(train=0.600, test=0.581) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=10, logistic_regression__C=0.01, poly_features__degree=3;, score=(train=0.602, test=0.575) total time=   0.2s\n",
      "[CV 1/5] END fselection__k=10, logistic_regression__C=0.1, poly_features__degree=1;, score=(train=0.778, test=0.787) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=10, logistic_regression__C=0.1, poly_features__degree=1;, score=(train=0.762, test=0.744) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=10, logistic_regression__C=0.1, poly_features__degree=1;, score=(train=0.762, test=0.838) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=10, logistic_regression__C=0.1, poly_features__degree=1;, score=(train=0.770, test=0.700) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=10, logistic_regression__C=0.1, poly_features__degree=1;, score=(train=0.777, test=0.775) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=10, logistic_regression__C=0.1, poly_features__degree=2;, score=(train=0.800, test=0.781) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=10, logistic_regression__C=0.1, poly_features__degree=2;, score=(train=0.689, test=0.713) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=10, logistic_regression__C=0.1, poly_features__degree=2;, score=(train=0.717, test=0.769) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=10, logistic_regression__C=0.1, poly_features__degree=2;, score=(train=0.728, test=0.675) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=10, logistic_regression__C=0.1, poly_features__degree=2;, score=(train=0.773, test=0.725) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=10, logistic_regression__C=0.1, poly_features__degree=3;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=10, logistic_regression__C=0.1, poly_features__degree=3;, score=(train=0.594, test=0.606) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=10, logistic_regression__C=0.1, poly_features__degree=3;, score=(train=0.592, test=0.613) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=10, logistic_regression__C=0.1, poly_features__degree=3;, score=(train=0.600, test=0.581) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=10, logistic_regression__C=0.1, poly_features__degree=3;, score=(train=0.602, test=0.575) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=10, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.809, test=0.794) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=10, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.809, test=0.787) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=10, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.792, test=0.875) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=10, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.823, test=0.731) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=10, logistic_regression__C=1, poly_features__degree=1;, score=(train=0.811, test=0.781) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=10, logistic_regression__C=1, poly_features__degree=2;, score=(train=0.834, test=0.787) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=10, logistic_regression__C=1, poly_features__degree=2;, score=(train=0.741, test=0.787) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=10, logistic_regression__C=1, poly_features__degree=2;, score=(train=0.780, test=0.844) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=10, logistic_regression__C=1, poly_features__degree=2;, score=(train=0.787, test=0.756) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=10, logistic_regression__C=1, poly_features__degree=2;, score=(train=0.792, test=0.762) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=10, logistic_regression__C=1, poly_features__degree=3;, score=(train=0.634, test=0.581) total time=   0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV 2/5] END fselection__k=10, logistic_regression__C=1, poly_features__degree=3;, score=(train=0.594, test=0.613) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=10, logistic_regression__C=1, poly_features__degree=3;, score=(train=0.595, test=0.613) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=10, logistic_regression__C=1, poly_features__degree=3;, score=(train=0.605, test=0.581) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=10, logistic_regression__C=1, poly_features__degree=3;, score=(train=0.602, test=0.575) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=10, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.816, test=0.787) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=10, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.800, test=0.806) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=10, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.794, test=0.869) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=10, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.822, test=0.750) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=10, logistic_regression__C=10, poly_features__degree=1;, score=(train=0.812, test=0.794) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=10, logistic_regression__C=10, poly_features__degree=2;, score=(train=0.847, test=0.800) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=10, logistic_regression__C=10, poly_features__degree=2;, score=(train=0.741, test=0.787) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=10, logistic_regression__C=10, poly_features__degree=2;, score=(train=0.794, test=0.825) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=10, logistic_regression__C=10, poly_features__degree=2;, score=(train=0.795, test=0.775) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=10, logistic_regression__C=10, poly_features__degree=2;, score=(train=0.809, test=0.769) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=10, logistic_regression__C=10, poly_features__degree=3;, score=(train=0.647, test=0.600) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=10, logistic_regression__C=10, poly_features__degree=3;, score=(train=0.609, test=0.619) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=10, logistic_regression__C=10, poly_features__degree=3;, score=(train=0.611, test=0.619) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=10, logistic_regression__C=10, poly_features__degree=3;, score=(train=0.617, test=0.588) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=10, logistic_regression__C=10, poly_features__degree=3;, score=(train=0.613, test=0.606) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=10, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.814, test=0.781) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=10, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.802, test=0.806) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=10, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.795, test=0.869) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=10, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.820, test=0.750) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=10, logistic_regression__C=100, poly_features__degree=1;, score=(train=0.806, test=0.794) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=10, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.850, test=0.806) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=10, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.741, test=0.787) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=10, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.791, test=0.825) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=10, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.792, test=0.781) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=10, logistic_regression__C=100, poly_features__degree=2;, score=(train=0.817, test=0.781) total time=   0.0s\n",
      "[CV 1/5] END fselection__k=10, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.648, test=0.594) total time=   0.0s\n",
      "[CV 2/5] END fselection__k=10, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.611, test=0.619) total time=   0.0s\n",
      "[CV 3/5] END fselection__k=10, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.613, test=0.619) total time=   0.0s\n",
      "[CV 4/5] END fselection__k=10, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.620, test=0.588) total time=   0.0s\n",
      "[CV 5/5] END fselection__k=10, logistic_regression__C=100, poly_features__degree=3;, score=(train=0.616, test=0.606) total time=   0.0s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('scaler', MinMaxScaler()),\n",
       "                                       ('poly_features', PolynomialFeatures()),\n",
       "                                       ('fselection',\n",
       "                                        SelectKBest(score_func=<function chi2 at 0x0000023038ED5310>)),\n",
       "                                       ('logistic_regression',\n",
       "                                        LogisticRegression(random_state=18))]),\n",
       "             param_grid={'fselection__k': (1, 2, 3, 4, 5, 6, 7, 8, 9, 10),\n",
       "                         'logistic_regression__C': (0.01, 0.1, 1, 10, 100),\n",
       "                         'poly_features__degree': (1, 2, 3)},\n",
       "             return_train_score=True, scoring='accuracy', verbose=3)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lreg = Pipeline([('scaler', MinMaxScaler()),\n",
    "                 ('poly_features', PolynomialFeatures()),\n",
    "                 ('fselection', SelectKBest(chi2)),\n",
    "                 ('logistic_regression', LogisticRegression(random_state = 18))])\n",
    "\n",
    "parameters = {'poly_features__degree':(1,2,3),\n",
    "              'fselection__k':(1,2,3,4,5,6,7,8,9,10),\n",
    "              'logistic_regression__C':(0.01,0.1,1,10,100)}\n",
    "\n",
    "grid_clf = GridSearchCV(lreg, \n",
    "                              parameters,\n",
    "                              cv = 5, \n",
    "                              verbose = 3,\n",
    "                              scoring = 'accuracy', \n",
    "                              return_train_score = True)\n",
    "grid_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "3036842d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'fselection__k': 7, 'logistic_regression__C': 100, 'poly_features__degree': 1}\n",
      "CV score: 0.8087500000000001\n",
      "The test data performance for the best rbf svm through grid search is: 84.00%\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.8172    0.8352    0.8261        91\n",
      "           1     0.8598    0.8440    0.8519       109\n",
      "\n",
      "    accuracy                         0.8400       200\n",
      "   macro avg     0.8385    0.8396    0.8390       200\n",
      "weighted avg     0.8404    0.8400    0.8401       200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Best Model\n",
    "print(grid_clf.best_params_) # best paramters\n",
    "print(f'CV score: {grid_clf.best_score_}')\n",
    "\n",
    "# Test\n",
    "y_test_grid = grid_clf.predict(X_test)\n",
    "acc_rbf_svm_grid = accuracy_score(y_test, y_test_grid)\n",
    "print(f\"The test data performance for the best rbf svm through grid search is: {acc_rbf_svm_grid:.2%}\")\n",
    "print(classification_report(y_test, y_test_grid, digits = 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e18c3a29",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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