732 lines
20 KiB
Plaintext
732 lines
20 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Chapter 6 – Decision Trees**"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"_This notebook contains all the sample code and solutions to the exercises in chapter 6._"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Setup"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"# To support both python 2 and python 3\n",
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"from __future__ import division, print_function, unicode_literals\n",
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"\n",
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"# Common imports\n",
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"import numpy as np\n",
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"import os\n",
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"\n",
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"# to make this notebook's output stable across runs\n",
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"np.random.seed(42)\n",
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"\n",
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"# To plot pretty figures\n",
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"%matplotlib inline\n",
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"import matplotlib\n",
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"import matplotlib.pyplot as plt\n",
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"plt.rcParams['axes.labelsize'] = 14\n",
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"plt.rcParams['xtick.labelsize'] = 12\n",
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"plt.rcParams['ytick.labelsize'] = 12\n",
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"\n",
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"# Where to save the figures\n",
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"PROJECT_ROOT_DIR = \".\"\n",
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"CHAPTER_ID = \"decision_trees\"\n",
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"\n",
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"def image_path(fig_id):\n",
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" return os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id)\n",
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"\n",
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"def save_fig(fig_id, tight_layout=True):\n",
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" print(\"Saving figure\", fig_id)\n",
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" if tight_layout:\n",
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" plt.tight_layout()\n",
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" plt.savefig(image_path(fig_id) + \".png\", format='png', dpi=300)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Training and visualizing"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.datasets import load_iris\n",
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"from sklearn.tree import DecisionTreeClassifier\n",
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"\n",
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"iris = load_iris()\n",
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"X = iris.data[:, 2:] # petal length and width\n",
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"y = iris.target\n",
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"\n",
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"tree_clf = DecisionTreeClassifier(max_depth=2, random_state=42)\n",
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"tree_clf.fit(X, y)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.tree import export_graphviz\n",
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"\n",
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"export_graphviz(\n",
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" tree_clf,\n",
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" out_file=image_path(\"iris_tree.dot\"),\n",
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" feature_names=iris.feature_names[2:],\n",
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" class_names=iris.target_names,\n",
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" rounded=True,\n",
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" filled=True\n",
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" )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"from matplotlib.colors import ListedColormap\n",
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"\n",
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"def plot_decision_boundary(clf, X, y, axes=[0, 7.5, 0, 3], iris=True, legend=False, plot_training=True):\n",
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" x1s = np.linspace(axes[0], axes[1], 100)\n",
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" x2s = np.linspace(axes[2], axes[3], 100)\n",
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" x1, x2 = np.meshgrid(x1s, x2s)\n",
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" X_new = np.c_[x1.ravel(), x2.ravel()]\n",
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" y_pred = clf.predict(X_new).reshape(x1.shape)\n",
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" custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])\n",
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" plt.contourf(x1, x2, y_pred, alpha=0.3, cmap=custom_cmap)\n",
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" if not iris:\n",
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" custom_cmap2 = ListedColormap(['#7d7d58','#4c4c7f','#507d50'])\n",
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" plt.contour(x1, x2, y_pred, cmap=custom_cmap2, alpha=0.8)\n",
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" if plot_training:\n",
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" plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", label=\"Iris-Setosa\")\n",
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" plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", label=\"Iris-Versicolor\")\n",
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" plt.plot(X[:, 0][y==2], X[:, 1][y==2], \"g^\", label=\"Iris-Virginica\")\n",
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" plt.axis(axes)\n",
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" if iris:\n",
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" plt.xlabel(\"Petal length\", fontsize=14)\n",
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" plt.ylabel(\"Petal width\", fontsize=14)\n",
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" else:\n",
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" plt.xlabel(r\"$x_1$\", fontsize=18)\n",
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" plt.ylabel(r\"$x_2$\", fontsize=18, rotation=0)\n",
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" if legend:\n",
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" plt.legend(loc=\"lower right\", fontsize=14)\n",
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"\n",
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"plt.figure(figsize=(8, 4))\n",
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"plot_decision_boundary(tree_clf, X, y)\n",
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"plt.plot([2.45, 2.45], [0, 3], \"k-\", linewidth=2)\n",
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"plt.plot([2.45, 7.5], [1.75, 1.75], \"k--\", linewidth=2)\n",
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"plt.plot([4.95, 4.95], [0, 1.75], \"k:\", linewidth=2)\n",
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"plt.plot([4.85, 4.85], [1.75, 3], \"k:\", linewidth=2)\n",
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"plt.text(1.40, 1.0, \"Depth=0\", fontsize=15)\n",
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"plt.text(3.2, 1.80, \"Depth=1\", fontsize=13)\n",
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"plt.text(4.05, 0.5, \"(Depth=2)\", fontsize=11)\n",
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"\n",
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"save_fig(\"decision_tree_decision_boundaries_plot\")\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Predicting classes and class probabilities"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"tree_clf.predict_proba([[5, 1.5]])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"tree_clf.predict([[5, 1.5]])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Sensitivity to training set details"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"X[(X[:, 1]==X[:, 1][y==1].max()) & (y==1)] # widest Iris-Versicolor flower"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"not_widest_versicolor = (X[:, 1]!=1.8) | (y==2)\n",
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"X_tweaked = X[not_widest_versicolor]\n",
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"y_tweaked = y[not_widest_versicolor]\n",
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"\n",
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"tree_clf_tweaked = DecisionTreeClassifier(max_depth=2, random_state=40)\n",
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"tree_clf_tweaked.fit(X_tweaked, y_tweaked)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.figure(figsize=(8, 4))\n",
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"plot_decision_boundary(tree_clf_tweaked, X_tweaked, y_tweaked, legend=False)\n",
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"plt.plot([0, 7.5], [0.8, 0.8], \"k-\", linewidth=2)\n",
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"plt.plot([0, 7.5], [1.75, 1.75], \"k--\", linewidth=2)\n",
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"plt.text(1.0, 0.9, \"Depth=0\", fontsize=15)\n",
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"plt.text(1.0, 1.80, \"Depth=1\", fontsize=13)\n",
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"\n",
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"save_fig(\"decision_tree_instability_plot\")\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.datasets import make_moons\n",
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"Xm, ym = make_moons(n_samples=100, noise=0.25, random_state=53)\n",
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"\n",
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"deep_tree_clf1 = DecisionTreeClassifier(random_state=42)\n",
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"deep_tree_clf2 = DecisionTreeClassifier(min_samples_leaf=4, random_state=42)\n",
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"deep_tree_clf1.fit(Xm, ym)\n",
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"deep_tree_clf2.fit(Xm, ym)\n",
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"\n",
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"plt.figure(figsize=(11, 4))\n",
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"plt.subplot(121)\n",
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"plot_decision_boundary(deep_tree_clf1, Xm, ym, axes=[-1.5, 2.5, -1, 1.5], iris=False)\n",
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"plt.title(\"No restrictions\", fontsize=16)\n",
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"plt.subplot(122)\n",
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"plot_decision_boundary(deep_tree_clf2, Xm, ym, axes=[-1.5, 2.5, -1, 1.5], iris=False)\n",
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"plt.title(\"min_samples_leaf = {}\".format(deep_tree_clf2.min_samples_leaf), fontsize=14)\n",
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"\n",
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"save_fig(\"min_samples_leaf_plot\")\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"angle = np.pi / 180 * 20\n",
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"rotation_matrix = np.array([[np.cos(angle), -np.sin(angle)], [np.sin(angle), np.cos(angle)]])\n",
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"Xr = X.dot(rotation_matrix)\n",
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"\n",
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"tree_clf_r = DecisionTreeClassifier(random_state=42)\n",
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"tree_clf_r.fit(Xr, y)\n",
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"\n",
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"plt.figure(figsize=(8, 3))\n",
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"plot_decision_boundary(tree_clf_r, Xr, y, axes=[0.5, 7.5, -1.0, 1], iris=False)\n",
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"\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"np.random.seed(6)\n",
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"Xs = np.random.rand(100, 2) - 0.5\n",
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"ys = (Xs[:, 0] > 0).astype(np.float32) * 2\n",
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"\n",
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"angle = np.pi / 4\n",
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"rotation_matrix = np.array([[np.cos(angle), -np.sin(angle)], [np.sin(angle), np.cos(angle)]])\n",
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"Xsr = Xs.dot(rotation_matrix)\n",
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"\n",
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"tree_clf_s = DecisionTreeClassifier(random_state=42)\n",
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"tree_clf_s.fit(Xs, ys)\n",
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"tree_clf_sr = DecisionTreeClassifier(random_state=42)\n",
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"tree_clf_sr.fit(Xsr, ys)\n",
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"\n",
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"plt.figure(figsize=(11, 4))\n",
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"plt.subplot(121)\n",
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"plot_decision_boundary(tree_clf_s, Xs, ys, axes=[-0.7, 0.7, -0.7, 0.7], iris=False)\n",
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"plt.subplot(122)\n",
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"plot_decision_boundary(tree_clf_sr, Xsr, ys, axes=[-0.7, 0.7, -0.7, 0.7], iris=False)\n",
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"\n",
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"save_fig(\"sensitivity_to_rotation_plot\")\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Regression trees"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Quadratic training set + noise\n",
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"np.random.seed(42)\n",
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"m = 200\n",
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"X = np.random.rand(m, 1)\n",
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"y = 4 * (X - 0.5) ** 2\n",
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"y = y + np.random.randn(m, 1) / 10"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.tree import DecisionTreeRegressor\n",
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"\n",
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"tree_reg = DecisionTreeRegressor(max_depth=2, random_state=42)\n",
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"tree_reg.fit(X, y)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.tree import DecisionTreeRegressor\n",
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"\n",
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"tree_reg1 = DecisionTreeRegressor(random_state=42, max_depth=2)\n",
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"tree_reg2 = DecisionTreeRegressor(random_state=42, max_depth=3)\n",
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"tree_reg1.fit(X, y)\n",
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"tree_reg2.fit(X, y)\n",
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"\n",
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"def plot_regression_predictions(tree_reg, X, y, axes=[0, 1, -0.2, 1], ylabel=\"$y$\"):\n",
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" x1 = np.linspace(axes[0], axes[1], 500).reshape(-1, 1)\n",
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" y_pred = tree_reg.predict(x1)\n",
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" plt.axis(axes)\n",
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" plt.xlabel(\"$x_1$\", fontsize=18)\n",
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" if ylabel:\n",
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" plt.ylabel(ylabel, fontsize=18, rotation=0)\n",
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" plt.plot(X, y, \"b.\")\n",
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" plt.plot(x1, y_pred, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
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"\n",
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"plt.figure(figsize=(11, 4))\n",
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"plt.subplot(121)\n",
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"plot_regression_predictions(tree_reg1, X, y)\n",
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"for split, style in ((0.1973, \"k-\"), (0.0917, \"k--\"), (0.7718, \"k--\")):\n",
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" plt.plot([split, split], [-0.2, 1], style, linewidth=2)\n",
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"plt.text(0.21, 0.65, \"Depth=0\", fontsize=15)\n",
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"plt.text(0.01, 0.2, \"Depth=1\", fontsize=13)\n",
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"plt.text(0.65, 0.8, \"Depth=1\", fontsize=13)\n",
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"plt.legend(loc=\"upper center\", fontsize=18)\n",
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"plt.title(\"max_depth=2\", fontsize=14)\n",
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"\n",
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"plt.subplot(122)\n",
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"plot_regression_predictions(tree_reg2, X, y, ylabel=None)\n",
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"for split, style in ((0.1973, \"k-\"), (0.0917, \"k--\"), (0.7718, \"k--\")):\n",
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" plt.plot([split, split], [-0.2, 1], style, linewidth=2)\n",
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"for split in (0.0458, 0.1298, 0.2873, 0.9040):\n",
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" plt.plot([split, split], [-0.2, 1], \"k:\", linewidth=1)\n",
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"plt.text(0.3, 0.5, \"Depth=2\", fontsize=13)\n",
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"plt.title(\"max_depth=3\", fontsize=14)\n",
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"\n",
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"save_fig(\"tree_regression_plot\")\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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"export_graphviz(\n",
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" tree_reg1,\n",
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" out_file=image_path(\"regression_tree.dot\"),\n",
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" feature_names=[\"x1\"],\n",
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" rounded=True,\n",
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" filled=True\n",
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" )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [],
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"source": [
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"tree_reg1 = DecisionTreeRegressor(random_state=42)\n",
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"tree_reg2 = DecisionTreeRegressor(random_state=42, min_samples_leaf=10)\n",
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"tree_reg1.fit(X, y)\n",
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"tree_reg2.fit(X, y)\n",
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"\n",
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"x1 = np.linspace(0, 1, 500).reshape(-1, 1)\n",
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"y_pred1 = tree_reg1.predict(x1)\n",
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"y_pred2 = tree_reg2.predict(x1)\n",
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"\n",
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"plt.figure(figsize=(11, 4))\n",
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"\n",
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"plt.subplot(121)\n",
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"plt.plot(X, y, \"b.\")\n",
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"plt.plot(x1, y_pred1, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
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"plt.axis([0, 1, -0.2, 1.1])\n",
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"plt.xlabel(\"$x_1$\", fontsize=18)\n",
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"plt.ylabel(\"$y$\", fontsize=18, rotation=0)\n",
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"plt.legend(loc=\"upper center\", fontsize=18)\n",
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"plt.title(\"No restrictions\", fontsize=14)\n",
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"\n",
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"plt.subplot(122)\n",
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"plt.plot(X, y, \"b.\")\n",
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"plt.plot(x1, y_pred2, \"r.-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
|
||
"plt.axis([0, 1, -0.2, 1.1])\n",
|
||
"plt.xlabel(\"$x_1$\", fontsize=18)\n",
|
||
"plt.title(\"min_samples_leaf={}\".format(tree_reg2.min_samples_leaf), fontsize=14)\n",
|
||
"\n",
|
||
"save_fig(\"tree_regression_regularization_plot\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"source": [
|
||
"# Exercise solutions"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 1. to 6."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"See appendix A."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"source": [
|
||
"## 7."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"_Exercise: train and fine-tune a Decision Tree for the moons dataset._"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"a. Generate a moons dataset using `make_moons(n_samples=10000, noise=0.4)`."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Adding `random_state=42` to make this notebook's output constant:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.datasets import make_moons\n",
|
||
"\n",
|
||
"X, y = make_moons(n_samples=10000, noise=0.4, random_state=42)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"b. Split it into a training set and a test set using `train_test_split()`."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.model_selection import train_test_split\n",
|
||
"\n",
|
||
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"c. Use grid search with cross-validation (with the help of the `GridSearchCV` class) to find good hyperparameter values for a `DecisionTreeClassifier`. Hint: try various values for `max_leaf_nodes`."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.model_selection import GridSearchCV\n",
|
||
"\n",
|
||
"params = {'max_leaf_nodes': list(range(2, 100)), 'min_samples_split': [2, 3, 4]}\n",
|
||
"grid_search_cv = GridSearchCV(DecisionTreeClassifier(random_state=42), params, n_jobs=-1, verbose=1)\n",
|
||
"\n",
|
||
"grid_search_cv.fit(X_train, y_train)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"grid_search_cv.best_estimator_"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"d. Train it on the full training set using these hyperparameters, and measure your model's performance on the test set. You should get roughly 85% to 87% accuracy."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"By default, `GridSearchCV` trains the best model found on the whole training set (you can change this by setting `refit=False`), so we don't need to do it again. We can simply evaluate the model's accuracy:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.metrics import accuracy_score\n",
|
||
"\n",
|
||
"y_pred = grid_search_cv.predict(X_test)\n",
|
||
"accuracy_score(y_test, y_pred)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 8."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"_Exercise: Grow a forest._"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"a. Continuing the previous exercise, generate 1,000 subsets of the training set, each containing 100 instances selected randomly. Hint: you can use Scikit-Learn's `ShuffleSplit` class for this."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.model_selection import ShuffleSplit\n",
|
||
"\n",
|
||
"n_trees = 1000\n",
|
||
"n_instances = 100\n",
|
||
"\n",
|
||
"mini_sets = []\n",
|
||
"\n",
|
||
"rs = ShuffleSplit(n_splits=n_trees, test_size=len(X_train) - n_instances, random_state=42)\n",
|
||
"for mini_train_index, mini_test_index in rs.split(X_train):\n",
|
||
" X_mini_train = X_train[mini_train_index]\n",
|
||
" y_mini_train = y_train[mini_train_index]\n",
|
||
" mini_sets.append((X_mini_train, y_mini_train))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"b. Train one Decision Tree on each subset, using the best hyperparameter values found above. Evaluate these 1,000 Decision Trees on the test set. Since they were trained on smaller sets, these Decision Trees will likely perform worse than the first Decision Tree, achieving only about 80% accuracy."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.base import clone\n",
|
||
"\n",
|
||
"forest = [clone(grid_search_cv.best_estimator_) for _ in range(n_trees)]\n",
|
||
"\n",
|
||
"accuracy_scores = []\n",
|
||
"\n",
|
||
"for tree, (X_mini_train, y_mini_train) in zip(forest, mini_sets):\n",
|
||
" tree.fit(X_mini_train, y_mini_train)\n",
|
||
" \n",
|
||
" y_pred = tree.predict(X_test)\n",
|
||
" accuracy_scores.append(accuracy_score(y_test, y_pred))\n",
|
||
"\n",
|
||
"np.mean(accuracy_scores)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"c. Now comes the magic. For each test set instance, generate the predictions of the 1,000 Decision Trees, and keep only the most frequent prediction (you can use SciPy's `mode()` function for this). This gives you _majority-vote predictions_ over the test set."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"Y_pred = np.empty([n_trees, len(X_test)], dtype=np.uint8)\n",
|
||
"\n",
|
||
"for tree_index, tree in enumerate(forest):\n",
|
||
" Y_pred[tree_index] = tree.predict(X_test)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from scipy.stats import mode\n",
|
||
"\n",
|
||
"y_pred_majority_votes, n_votes = mode(Y_pred, axis=0)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"d. Evaluate these predictions on the test set: you should obtain a slightly higher accuracy than your first model (about 0.5 to 1.5% higher). Congratulations, you have trained a Random Forest classifier!"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"accuracy_score(y_test, y_pred_majority_votes.reshape([-1]))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.5.2"
|
||
},
|
||
"nav_menu": {
|
||
"height": "309px",
|
||
"width": "468px"
|
||
},
|
||
"toc": {
|
||
"navigate_menu": true,
|
||
"number_sections": true,
|
||
"sideBar": true,
|
||
"threshold": 6,
|
||
"toc_cell": false,
|
||
"toc_section_display": "block",
|
||
"toc_window_display": false
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 1
|
||
}
|