sitc/ml1/2_5_2_Decision_Tree_Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](files/images/EscUpmPolit_p.gif \"UPM\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Course Notes for Learning Intelligent Systems"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © Carlos A. Iglesias"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [Introduction to Machine Learning](2_0_0_Intro_ML.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table of Contents\n",
    "* [Decision Tree Learning](#Decision-Tree-Learning)\n",
    "* [Load data and preprocessing](#Load-data-and-preprocessing)\n",
    "* [Train classifier](#Train-classifier)\n",
    "* [Evaluating the algorithm](#Evaluating-the-algorithm)\n",
    "\t* [Precision, recall and f-score](#Precision,-recall-and-f-score)\n",
    "\t* [Confusion matrix](#Confusion-matrix)\n",
    "\t* [K-Fold cross validation](#K-Fold-cross-validation)\n",
    "* [References](#References)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal of this notebook is to learn how to create a classification object using a [decision tree learning algorithm](https://en.wikipedia.org/wiki/Decision_tree_learning). \n",
    "\n",
    "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",
    "\n",
    "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 peculiarities of the decision tree algorithms.\n",
    "\n",
    "You need to install pydotplus: `conda install pydotplus` for the visualization."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data and preprocessing\n",
    "\n",
    "Here we repeat the same operations for loading data and preprocessing than in the previous notebooks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/cif/anaconda3/lib/python3.5/site-packages/sklearn/utils/fixes.py:313: FutureWarning: numpy not_equal will not check object identity in the future. The comparison did not return the same result as suggested by the identity (`is`)) and will change.\n",
      "  _nan_object_mask = _nan_object_array != _nan_object_array\n"
     ]
    }
   ],
   "source": [
    "# library for displaying plots\n",
    "import matplotlib.pyplot as plt\n",
    "# display plots in the notebook \n",
    "%matplotlib inline\n",
    "\n",
    "## First, we repeat the load and preprocessing steps\n",
    "\n",
    "# Load data\n",
    "from sklearn import datasets\n",
    "iris = datasets.load_iris()\n",
    "\n",
    "# Training and test spliting\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_iris, y_iris = iris.data, iris.target\n",
    "# Test set will be the 25% taken randomly\n",
    "x_train, x_test, y_train, y_test = train_test_split(x_iris, y_iris, test_size=0.25, random_state=33)\n",
    "\n",
    "# Preprocess: normalize\n",
    "from sklearn import preprocessing\n",
    "scaler = preprocessing.StandardScaler().fit(x_train)\n",
    "x_train = scaler.transform(x_train)\n",
    "x_test = scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Train classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The usual steps for creating a classifier are:\n",
    "1. Create classifier object\n",
    "2. Call *fit* to train the classifier\n",
    "3. Call *predict* to obtain predictions\n",
    "\n",
    "*DecisionTreeClassifier* is capable of both binary (where the labels are [-1, 1]) classification and multiclass (where the labels are [0, ..., K-1]) classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=3,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=1,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import numpy as np\n",
    "\n",
    "from sklearn import tree\n",
    "\n",
    "max_depth=3\n",
    "random_state=1\n",
    "\n",
    "# Create decision tree model\n",
    "model = tree.DecisionTreeClassifier(max_depth=max_depth, random_state=random_state)\n",
    "\n",
    "# Train the model using the training sets\n",
    "model.fit(x_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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",
      " 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",
      " 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",
      " 0]\n",
      "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",
      " 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",
      " 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",
      " 0]\n"
     ]
    }
   ],
   "source": [
    "print(\"Prediction \", model.predict(x_train))\n",
    "print(\"Expected \", y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, the probability of each class can be predicted, which is the fraction of training samples of the same class in a leaf:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted probabilities [[ 0.          0.97368421  0.02631579]\n",
      " [ 1.          0.          0.        ]\n",
      " [ 0.          0.97368421  0.02631579]\n",
      " [ 0.          0.97368421  0.02631579]\n",
      " [ 0.          0.97368421  0.02631579]\n",
      " [ 1.          0.          0.        ]\n",
      " [ 1.          0.          0.        ]\n",
      " [ 0.          0.97368421  0.02631579]\n",
      " [ 1.          0.          0.        ]\n",
      " [ 0.          0.          1.        ]]\n"
     ]
    }
   ],
   "source": [
    "# Print the \n",
    "print(\"Predicted probabilities\", model.predict_proba(x_train[:10]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy in training 0.982142857143\n"
     ]
    }
   ],
   "source": [
    "# Evaluate Accuracy in training\n",
    "\n",
    "from sklearn import metrics\n",
    "y_train_pred = model.predict(x_train)\n",
    "print(\"Accuracy in training\", metrics.accuracy_score(y_train, y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy in testing  0.921052631579\n"
     ]
    }
   ],
   "source": [
    "# Now we evaluate error in testing\n",
    "y_test_pred = model.predict(x_test)\n",
    "print(\"Accuracy in testing \", metrics.accuracy_score(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are going to visualize the DecisionTree classification. It will plot the decision boundaries for each class.\n",
    "\n",
    "The current version of pydot does not work well in Python 3.\n",
    "For obtaining an image, you need to install `pip install pydotplus` and then `conda install graphviz`.\n",
    "\n",
    "You can skip this example. Since it can require installing additional packages, we include here the result.\n",
    "![Decision Tree](files/images/cart.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "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": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'matplotlib.colors' has no attribute 'to_rgba'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-7a926dac062b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# display plots in the notebook\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mplot_tree_iris\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/GoogleDrive/cursos/sitc/sitc/ml1/util_ds.py\u001b[0m in \u001b[0;36mplot_tree_iris\u001b[0;34m()\u001b[0m\n\u001b[1;32m    115\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msuptitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Decision surface of a decision tree using paired features\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m    242\u001b[0m     \"\"\"\n\u001b[1;32m    243\u001b[0m     \u001b[0;32mglobal\u001b[0m \u001b[0m_show\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 244\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_show\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    245\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m     37\u001b[0m             display(\n\u001b[1;32m     38\u001b[0m                 \u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m                 \u001b[0mmetadata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_fetch_figure_metadata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigure_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m             )\n\u001b[1;32m     41\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36m_fetch_figure_metadata\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    172\u001b[0m     \u001b[0;34m\"\"\"Get some metadata to help with displaying a figure.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    173\u001b[0m     \u001b[0;31m# determine if a background is needed for legibility\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 174\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0m_is_transparent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_facecolor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    175\u001b[0m         \u001b[0;31m# the background is transparent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    176\u001b[0m         ticksLight = _is_light([label.get_color()\n",
      "\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/ipykernel/pylab/backend_inline.py\u001b[0m in \u001b[0;36m_is_transparent\u001b[0;34m(color)\u001b[0m\n\u001b[1;32m    193\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_is_transparent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    194\u001b[0m     \u001b[0;34m\"\"\"Determine transparency from alpha.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 195\u001b[0;31m     \u001b[0mrgba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_rgba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    196\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mrgba\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m.5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: module 'matplotlib.colors' has no attribute 'to_rgba'"
     ]
    }
   ],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection 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",
    "cv = KFold(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": null,
   "metadata": {},
   "outputs": [],
   "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",
    "© Carlos A. Iglesias, Universidad Politécnica de Madrid."
   ]
  }
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