ch05-线性回归/02-多元线性回归.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 多元线性回归\n",
    "- [使用自己制作Linear Regression](#使用自己制作Linear_Regression)\n",
    "\n",
    "只要记结论即可，用到随手一查\n",
    "![](images/07.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston = datasets.load_boston()\n",
    "\n",
    "X = boston.data\n",
    "y = boston.target\n",
    "\n",
    "X = X[y < 50.0]\n",
    "y = y[y < 50.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(490, 13)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from playML.model_selection import train_test_split\n",
    "# 使用相同的随机种子，结果一致\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, seed=666) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用自己制作Linear_Regression\n",
    "\n",
    "代码参见 [这里](playML/LinearRegression.py)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from playML.LinearRegression import LinearRegression\n",
    "\n",
    "reg = LinearRegression()\n",
    "reg.fit_normal(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.20354261e-01,  3.64423279e-02, -3.61493155e-02,  5.12978140e-02,\n",
       "       -1.15775825e+01,  3.42740062e+00, -2.32311760e-02, -1.19487594e+00,\n",
       "        2.60101728e-01, -1.40219119e-02, -8.35430488e-01,  7.80472852e-03,\n",
       "       -3.80923751e-01])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34.11739972320805"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8129794056212925"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/02.png)\n",
    "![](images/03.png)\n",
    "![](images/04.png)\n",
    "![](images/05.png)\n",
    "![](images/06.png)"
   ]
  }
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