From adddd4680604740a6e5949938e616e5f52e9db76 Mon Sep 17 00:00:00 2001
From: Andrea Cantu <a01235000@tec.mx>
Date: Mon, 14 Oct 2024 15:08:45 -0600
Subject: [PATCH] Se agregaron parametros al modelo

---
 notebooks/BoneMarrow_EDA.ipynb | 78 ++++++++++++++++++++++++++++++++++
 1 file changed, 78 insertions(+)

diff --git a/notebooks/BoneMarrow_EDA.ipynb b/notebooks/BoneMarrow_EDA.ipynb
index 6a28098..16de88a 100644
--- a/notebooks/BoneMarrow_EDA.ipynb
+++ b/notebooks/BoneMarrow_EDA.ipynb
@@ -1251,6 +1251,84 @@
    "metadata": {},
    "outputs": [],
    "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a78103c8",
+   "metadata": {},
+   "source": [
+    "## Fase 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e303b3e",
+   "metadata": {},
+   "source": [
+    "## Seguimiento de Experimentos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "24946793",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = RandomForestClassifier(n_estimators=90, max_depth=7, max_features=3)\n",
+    "model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d40ab43c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b5ee6301",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Confusion matrix\n",
+    "cm = confusion_matrix(y_test, y_pred)\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=np.unique(y), yticklabels=np.unique(y))\n",
+    "plt.xlabel('Predicted')\n",
+    "plt.ylabel('True')\n",
+    "plt.title('Confusion Matrix')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6fa64ab0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Report\n",
+    "report = classification_report(y_test, y_pred, output_dict=True)\n",
+    "report_df = pd.DataFrame(report).transpose()\n",
+    "print(report_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "007141ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scores = cross_val_score(model, X, y, cv=5)\n",
+    "print(\"Average accuracy with CV:\", np.mean(scores))"
+   ]
   }
  ],
  "metadata": {