{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy on the training subset: 1.000\n", "Accuracy on the test subset: 0.972\n" ] } ], "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.datasets import load_breast_cancer\n", "\n", "cancer = load_breast_cancer()\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target, random_state=0)\n", "\n", "forest = RandomForestClassifier(n_estimators=100, random_state=0)\n", "forest.fit(X_train, y_train)\n", "\n", "print('Accuracy on the training subset: {:.3f}'.format(forest.score(X_train, y_train)))\n", "print('Accuracy on the test subset: {:.3f}'.format(forest.score(X_test, y_test)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "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": "3.5.0" } }, "nbformat": 4, "nbformat_minor": 2 }