mirror of
https://github.com/gsi-upm/sitc
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674 lines
214 KiB
Plaintext
674 lines
214 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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"![](files/images/EscUpmPolit_p.gif \"UPM\")"
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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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"# Course Notes for Learning Intelligent Systems"
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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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"Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © 2016 Carlos A. Iglesias"
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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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"## [Introduction to Machine Learning](2_0_0_Intro_ML.ipynb)"
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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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"# Table of Contents\n",
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"* [Decision Tree Learning](#Decision-Tree-Learning)\n",
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"* [Load data and preprocessing](#Load-data-and-preprocessing)\n",
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"* [Train classifier](#Train-classifier)\n",
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"* [Evaluating the algorithm](#Evaluating-the-algorithm)\n",
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"\t* [Precision, recall and f-score](#Precision,-recall-and-f-score)\n",
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"\t* [Confusion matrix](#Confusion-matrix)\n",
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"\t* [K-Fold cross validation](#K-Fold-cross-validation)\n",
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"* [References](#References)"
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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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"# Decision Tree Learning"
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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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"The goal of this notebook is to learn how to learn how create a classification object using a [decision tree learning algorithm](https://en.wikipedia.org/wiki/Decision_tree_learning). \n",
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"\n",
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"There are a number of well known machine learning algorithms for decision tree learning, such as ID3, C4.5, C5.0 and CART. The scikit-learn uses an optimised version of the [CART (Classification and Regression Trees) algorithm](https://en.wikipedia.org/wiki/Predictive_analytics#Classification_and_regression_trees).\n",
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"\n",
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"This notebook will follow the same steps that the previous notebook for learning using the [kNN Model](2_5_1_kNN_Model.ipynb), and details some pecualiarities of the decision tree algorithms."
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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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"## Load data and preprocessing\n",
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"\n",
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"Here we repeat the same operations for loading data and preprocessing than in the previous notebooks."
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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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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# library for displaying plots\n",
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"import matplotlib.pyplot as plt\n",
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"# display plots in the notebook \n",
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"%matplotlib inline\n",
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"\n",
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"## First, we repeat the load and preprocessing steps\n",
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"\n",
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"# Load data\n",
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"from sklearn import datasets\n",
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"iris = datasets.load_iris()\n",
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"\n",
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"# Training and test spliting\n",
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"from sklearn.cross_validation import train_test_split\n",
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"\n",
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"x_iris, y_iris = iris.data, iris.target\n",
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"# Test set will be the 25% taken randomly\n",
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"x_train, x_test, y_train, y_test = train_test_split(x_iris, y_iris, test_size=0.25, random_state=33)\n",
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"\n",
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"# Preprocess: normalize\n",
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"from sklearn import preprocessing\n",
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"scaler = preprocessing.StandardScaler().fit(x_train)\n",
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"x_train = scaler.transform(x_train)\n",
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"x_test = scaler.transform(x_test)"
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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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"collapsed": true
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},
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"source": [
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"## Train classifier"
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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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"The usual steps for creating a classifier are:\n",
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"1. Create classifier object\n",
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"2. Call *fit* to train the classifier\n",
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"3. Call *predict* to obtain predictions\n",
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"\n",
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"*DecisionTreeClassifier* is capable of both binary (where the labels are [-1, 1]) classification and multiclass (where the labels are [0, ..., K-1]) classification."
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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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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=3,\n",
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" max_features=None, max_leaf_nodes=None, min_samples_leaf=1,\n",
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" min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
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" presort=False, random_state=1, splitter='best')"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from sklearn.tree import DecisionTreeClassifier\n",
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"import numpy as np\n",
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"\n",
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"from sklearn import tree\n",
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"\n",
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"max_depth=3\n",
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"random_state=1\n",
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"\n",
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"# Create decision tree model\n",
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"model = tree.DecisionTreeClassifier(max_depth=max_depth, random_state=random_state)\n",
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"\n",
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"# Train the model using the training sets\n",
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"model.fit(x_train, y_train) "
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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": 31,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Prediction [1 0 1 1 1 0 0 1 0 2 0 0 1 2 0 1 2 2 1 1 0 0 2 0 0 2 1 1 2 2 2 2 0 0 1 1 0\n",
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" 1 2 1 2 0 2 0 1 0 2 1 0 2 2 0 0 2 0 0 0 2 2 0 1 0 1 0 1 1 1 1 1 0 1 0 1 2\n",
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" 0 0 0 0 2 2 0 1 1 2 1 0 0 2 1 1 0 1 1 0 2 1 2 1 2 0 1 0 0 0 2 1 2 1 2 1 2\n",
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" 0]\n",
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"Expected [1 0 1 1 1 0 0 1 0 2 0 0 1 2 0 1 2 2 1 1 0 0 2 0 0 2 1 1 2 2 2 2 0 0 1 1 0\n",
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" 1 2 1 2 0 2 0 1 0 2 1 0 2 2 0 0 2 0 0 0 2 2 0 1 0 1 0 1 1 1 1 1 0 1 0 1 2\n",
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" 0 0 0 0 2 2 0 1 1 2 1 0 0 1 1 1 0 1 1 0 2 2 2 1 2 0 1 0 0 0 2 1 2 1 2 1 2\n",
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" 0]\n"
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]
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}
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],
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"source": [
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"print(\"Prediction \", model.predict(x_train))\n",
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"print(\"Expected \", y_train)"
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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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"Alternatively, the probability of each class can be predicted, which is the fraction of training samples of the same class in a leaf:"
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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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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Predicted probabilities [[ 0. 0.97368421 0.02631579]\n",
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" [ 1. 0. 0. ]\n",
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" [ 0. 0.97368421 0.02631579]\n",
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" [ 0. 0.97368421 0.02631579]\n",
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" [ 0. 0.97368421 0.02631579]\n",
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" [ 1. 0. 0. ]\n",
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" [ 1. 0. 0. ]\n",
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" [ 0. 0.97368421 0.02631579]\n",
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" [ 1. 0. 0. ]\n",
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" [ 0. 0. 1. ]]\n"
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]
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}
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],
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"source": [
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"# Print the \n",
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"print(\"Predicted probabilities\", model.predict_proba(x_train[: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": 8,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Accuracy in training 0.982142857143\n"
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]
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}
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],
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"source": [
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"# Evaluate Accuracy in training\n",
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"\n",
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"from sklearn import metrics\n",
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"y_train_pred = model.predict(x_train)\n",
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"print(\"Accuracy in training\", metrics.accuracy_score(y_train, y_train_pred))"
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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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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Accuracy in testing 0.921052631579\n"
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]
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}
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],
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"source": [
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"# Now we evaluate error in testing\n",
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"y_test_pred = model.predict(x_test)\n",
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"print(\"Accuracy in testing \", metrics.accuracy_score(y_test, y_test_pred))"
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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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"Now we are going to visualize the DecisionTree classification. It will plot the decision boundaries for each class.\n",
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"\n",
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"The current version of pydot does not work well in Python 3.\n",
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"For obtaining an image, you need to install `pip install pydotplus` and then `conda install graphviz`.\n",
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"\n",
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"You can skip this example. Since it can require installing additional packages, we include here the result.\n",
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"![Decision Tree](files/images/cart.png)"
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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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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"image/png": 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X2U5RlHQT8fSo1WoePYpItbPM/QcPiIiIxLF88o4ejuXLA/A04iFJ8bGpjriX6e8a4zlv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\n4I+QR68GeA9vqYvhcLufSDgZS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/YW7jjlcwfUooxhSH9ClCZJ3kWRaVRqLD0deWHJW1z7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\nTkSuQmCC\n",
|
|
"text/plain": [
|
|
"<IPython.core.display.Image object>"
|
|
]
|
|
},
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"from IPython.display import Image \n",
|
|
"from sklearn.externals.six import StringIO\n",
|
|
"import pydotplus as pydot\n",
|
|
"\n",
|
|
"dot_data = StringIO() \n",
|
|
"tree.export_graphviz(model, out_file=dot_data, \n",
|
|
" feature_names=iris.feature_names, \n",
|
|
" class_names=iris.target_names, \n",
|
|
" filled=True, rounded=True, \n",
|
|
" special_characters=True) \n",
|
|
"\n",
|
|
"\n",
|
|
"graph = pydot.graph_from_dot_data(dot_data.getvalue()) \n",
|
|
"graph.write_png('iris-tree.png')\n",
|
|
"Image(graph.create_png()) "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Here we show a graph of the decision tree boundaries. For each pair of iris features, the decision tree learns decision boundaries made of combinations of simple thresholding rules inferred from the training samples.\n",
|
|
"\n",
|
|
"We are going to import a function defined in the file [util_ds.py](files/util_ds.py) using the *magic command* **%run**."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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WlbEJ3C1b4Omn1V89K2gaREdDjiLmMHw4fPihEg4REcpNpHcHTZks+Ou1g/nr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EDSx83s9kYXLOlKwud+rxFeoznNzLY0upxmU8+JuRFDU1npX3w5E6adyPq7FwIwdr8DWD3/Ox17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\nVwAAAABJRU5ErkJggg==\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7fddf036ed30>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"%run util_ds\n",
|
|
"\n",
|
|
"# display plots in the notebook \n",
|
|
"%matplotlib inline\n",
|
|
"plot_tree_iris()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Next we are going to export the pseudocode of the the learnt decision tree."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"if ( petal width (cm) <= -0.415243178606 ) {\n",
|
|
" return setosa ( 42 examples )\n",
|
|
"}\n",
|
|
"else {\n",
|
|
" if ( petal width (cm) <= 0.861689627171 ) {\n",
|
|
" if ( petal length (cm) <= 0.818572163582 ) {\n",
|
|
" return versicolor ( 37 examples )\n",
|
|
" return virginica ( 1 examples )\n",
|
|
" }\n",
|
|
" else {\n",
|
|
" return versicolor ( 1 examples )\n",
|
|
" return virginica ( 2 examples )\n",
|
|
" }\n",
|
|
" }\n",
|
|
" else {\n",
|
|
" if ( petal length (cm) <= 0.707377433777 ) {\n",
|
|
" return versicolor ( 1 examples )\n",
|
|
" }\n",
|
|
" else {\n",
|
|
" return virginica ( 28 examples )\n",
|
|
" }\n",
|
|
" }\n",
|
|
"}\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"%run util_ds\n",
|
|
"get_code(model, iris.feature_names, iris.target_names)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can also obtain the feature importance of the fitted model as follows."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']\n",
|
|
"[ 0. 0. 0.05947455 0.94052545]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(iris.feature_names)\n",
|
|
"print(model.feature_importances_)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We see that the most important feature for this classifier is `petal width`."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Evaluating the algorithm"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Precision, recall and f-score"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"For evaluating classification algorithms, we usually calculate three metrics: precision, recall and F1-score\n",
|
|
"\n",
|
|
"* **Precision**: This computes the proportion of instances predicted as positives that were correctly evaluated (it measures how right our classifier is when it says that an instance is positive).\n",
|
|
"* **Recall**: This counts the proportion of positive instances that were correctly evaluated (measuring how right our classifier is when faced with a positive instance).\n",
|
|
"* **F1-score**: This is the harmonic mean of precision and recall, and tries to combine both in a single number."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
" precision recall f1-score support\n",
|
|
"\n",
|
|
" setosa 1.00 1.00 1.00 8\n",
|
|
" versicolor 0.79 1.00 0.88 11\n",
|
|
" virginica 1.00 0.84 0.91 19\n",
|
|
"\n",
|
|
"avg / total 0.94 0.92 0.92 38\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(metrics.classification_report(y_test, y_test_pred,target_names=iris.target_names))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Confusion matrix"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Another useful metric is the confusion matrix"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[[ 8 0 0]\n",
|
|
" [ 0 11 0]\n",
|
|
" [ 0 3 16]]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(metrics.confusion_matrix(y_test, y_test_pred))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"We see we classify well all the 'setosa' and 'versicolor' samples. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### K-Fold cross validation"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"In order to avoid bias in the training and testing dataset partition, it is recommended to use **k-fold validation**.\n",
|
|
"\n",
|
|
"Sklearn comes with other strategies for [cross validation](http://scikit-learn.org/stable/modules/cross_validation.html#cross-validation), such as stratified K-fold, label k-fold, Leave-One-Out, Leave-P-Out, Leave-One-Label-Out, Leave-P-Label-Out or Shuffle & Split."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[ 1. 0.8 1. 0.93333333 0.93333333 1. 1.\n",
|
|
" 1. 0.86666667 0.93333333]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from sklearn.cross_validation import cross_val_score, KFold\n",
|
|
"from sklearn.pipeline import Pipeline\n",
|
|
"from sklearn.preprocessing import StandardScaler\n",
|
|
"\n",
|
|
"# create a composite estimator made by a pipeline of preprocessing and the KNN model\n",
|
|
"model = Pipeline([\n",
|
|
" ('scaler', StandardScaler()),\n",
|
|
" ('DecisionTree', DecisionTreeClassifier())\n",
|
|
"])\n",
|
|
"\n",
|
|
"# create a k-fold cross validation iterator of k=10 folds\n",
|
|
"\n",
|
|
"cv = KFold(x_iris.shape[0], 10, shuffle=True, random_state=33)\n",
|
|
"\n",
|
|
"# by default the score used is the one returned by score method of the estimator (accuracy)\n",
|
|
"scores = cross_val_score(model, x_iris, y_iris, cv=cv)\n",
|
|
"print(scores)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"source": [
|
|
"We get an array of k scores. We can calculate the mean and the standard error to obtain a final figure"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Mean score: 0.947 (+/- 0.022)\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from scipy.stats import sem\n",
|
|
"def mean_score(scores):\n",
|
|
" return (\"Mean score: {0:.3f} (+/- {1:.3f})\").format(np.mean(scores), sem(scores))\n",
|
|
"print(mean_score(scores))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"So, we get an average accuracy of 0.947."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## References"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"* [Plot the decision surface of a decision tree on the iris dataset](http://scikit-learn.org/stable/auto_examples/tree/plot_iris.html)\n",
|
|
"* [Learning scikit-learn: Machine Learning in Python](http://proquest.safaribooksonline.com/book/programming/python/9781783281930/1dot-machine-learning-a-gentle-introduction/ch01s02_html), Raúl Garreta; Guillermo Moncecchi, Packt Publishing, 2013.\n",
|
|
"* [Python Machine Learning](http://proquest.safaribooksonline.com/book/programming/python/9781783555130), Sebastian Raschka, Packt Publishing, 2015.\n",
|
|
"* [Parameter estimation using grid search with cross-validation](http://scikit-learn.org/stable/auto_examples/model_selection/grid_search_digits.html)\n",
|
|
"* [Decision trees in python with scikit-learn and pandas](http://chrisstrelioff.ws/sandbox/2015/06/08/decision_trees_in_python_with_scikit_learn_and_pandas.html)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Licence\n",
|
|
"The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n",
|
|
"\n",
|
|
"© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid."
|
|
]
|
|
}
|
|
],
|
|
"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": "3.5.1+"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
}
|