model_evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing machine learning models in scikit-learn\n",
    "*From the video series: [Introduction to machine learning with scikit-learn](https://github.com/justmarkham/scikit-learn-videos)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# read in the iris data\n",
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()\n",
    "\n",
    "# create X (features) and y (response)\n",
    "X = iris.data\n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1,\n",
       "       1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import the class\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# instantiate the model (using the default parameters)\n",
    "logreg = LogisticRegression()\n",
    "\n",
    "# fit the model with data\n",
    "logreg.fit(X, y)\n",
    "\n",
    "# predict the response values for the observations in X\n",
    "logreg.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "150"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# store the predicted response values\n",
    "y_pred = logreg.predict(X)\n",
    "\n",
    "# check how many predictions were generated\n",
    "len(y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Classification accuracy:\n",
    "\n",
    "- **Proportion** of correct predictions\n",
    "- Common **evaluation metric** for classification problems"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.96\n"
     ]
    }
   ],
   "source": [
    "# compute classification accuracy for the logistic regression model\n",
    "from sklearn import metrics\n",
    "print metrics.accuracy_score(y, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Known as **training accuracy** when you train and test the model on the same data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN (K=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.966666666667\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "knn.fit(X, y)\n",
    "y_pred = knn.predict(X)\n",
    "print metrics.accuracy_score(y, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN (K=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=1)\n",
    "knn.fit(X, y)\n",
    "y_pred = knn.predict(X)\n",
    "print metrics.accuracy_score(y, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "targets = dict(zip(range(3), iris.target_names))\n",
    "features = dict(zip(range(4), iris.feature_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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fX29Cb0KIWCfX3IWIkO4WC9Um9VUD2OPjTepNCCFaJsFdCD8y09MpN6mvMiAr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115e9f490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = X\n",
    "feature1 = 0\n",
    "feature2 = 1\n",
    "f1vals = X[:,feature1]\n",
    "f2vals = X[:,feature2]\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "colors = ['r', 'g', 'b']\n",
    "fig = plt.figure(figsize=(8,8))\n",
    "ax = plt.subplot()\n",
    "for species in targets.keys():\n",
    "    f1 = f1vals[np.where(y==species)]\n",
    "    f2 = f2vals[np.where(y==species)]\n",
    "    ax.scatter(f1, f2, c=colors[species], label=classes[species], s=400)\n",
    "    ax.set(xlabel=features[feature1], ylabel=features[feature2])\n",
    "    ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problems with training and testing on the same data\n",
    "\n",
    "- Goal is to estimate likely performance of a model on **out-of-sample data**\n",
    "- But, maximizing training accuracy rewards **overly complex models** that won't necessarily generalize\n",
    "- Unnecessarily complex models **overfit** the training data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Overfitting](images/05_overfitting.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Image Credit: [Overfitting](http://commons.wikimedia.org/wiki/File:Overfitting.svg#/media/File:Overfitting.svg) by Chabacano. Licensed under GFDL via Wikimedia Commons.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation procedure #2: Train/test split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Split the dataset into two pieces: a **training set** and a **testing set**.\n",
    "2. Train the model on the **training set**.\n",
    "3. Test the model on the **testing set**, and evaluate how well we did."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 4)\n",
      "(150,)\n"
     ]
    }
   ],
   "source": [
    "# print the shapes of X and y\n",
    "print X.shape\n",
    "print y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# STEP 1: split X and y into training and testing sets\n",
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Train/test split](images/05_train_test_split.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What did this accomplish?\n",
    "\n",
    "- Model can be trained and tested on **different data**\n",
    "- Response values are known for the testing set, and thus **predictions can be evaluated**\n",
    "- **Testing accuracy** is a better estimate than training accuracy of out-of-sample performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(90, 4)\n",
      "(60, 4)\n"
     ]
    }
   ],
   "source": [
    "# print the shapes of the new X objects\n",
    "print X_train.shape\n",
    "print X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(90,)\n",
      "(60,)\n"
     ]
    }
   ],
   "source": [
    "# print the shapes of the new y objects\n",
    "print y_train.shape\n",
    "print y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# STEP 2: train the model on the training set\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.95\n"
     ]
    }
   ],
   "source": [
    "# STEP 3: make predictions on the testing set\n",
    "y_pred = logreg.predict(X_test)\n",
    "\n",
    "# compare actual response values (y_test) with predicted response values (y_pred)\n",
    "print metrics.accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Repeat for KNN with K=5:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.966666666667\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "knn.fit(X_train, y_train)\n",
    "y_pred = knn.predict(X_test)\n",
    "print metrics.accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Repeat for KNN with K=1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.95\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=1)\n",
    "knn.fit(X_train, y_train)\n",
    "y_pred = knn.predict(X_test)\n",
    "print metrics.accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can we locate an even better value for K?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# try K=1 through K=25 and record testing accuracy\n",
    "k_range = range(1, 26)\n",
    "scores = []\n",
    "for k in k_range:\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    knn.fit(X_train, y_train)\n",
    "    y_pred = knn.predict(X_test)\n",
    "    scores.append(metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1158b7d10>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YAzwWEd0RsQe4CTi/YpvZJAGBiFgNzJQ0IX2641np44OJiL0R8Wy6z/nAkvT1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\nw6JECwTkHloAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111da6a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import Matplotlib (scientific plotting library)\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# allow plots to appear within the notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# plot the relationship between K and testing accuracy\n",
    "plt.plot(k_range, scores)\n",
    "plt.xlabel('Value of K for KNN')\n",
    "plt.ylabel('Testing Accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Training accuracy** rises as model complexity increases\n",
    "- **Testing accuracy** penalizes models that are too complex or not complex enough\n",
    "- For KNN models, complexity is determined by the **value of K** (lower value = more complex)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making predictions on out-of-sample data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dtamayo/anaconda/envs/ml/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  DeprecationWarning)\n"
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   "source": [
    "# instantiate the model with the best known parameters\n",
    "knn = KNeighborsClassifier(n_neighbors=11)\n",
    "\n",
    "# train the model with X and y (not X_train and y_train)\n",
    "knn.fit(X, y)\n",
    "\n",
    "# make a prediction for an out-of-sample observation\n",
    "knn.predict([3, 5, 4, 2])"
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    "## Downsides of train/test split?"
   ]
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    "- Provides a **high-variance estimate** of out-of-sample accuracy\n",
    "- **K-fold cross-validation** overcomes this limitation\n",
    "- But, train/test split is still useful because of its **flexibility and speed**"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resources\n",
    "\n",
    "- Quora: [What is an intuitive explanation of overfitting?](http://www.quora.com/What-is-an-intuitive-explanation-of-overfitting/answer/Jessica-Su)\n",
    "- Video: [Estimating prediction error](https://www.youtube.com/watch?v=_2ij6eaaSl0&t=2m34s) (12 minutes, starting at 2:34) by Hastie and Tibshirani\n",
    "- [Understanding the Bias-Variance Tradeoff](http://scott.fortmann-roe.com/docs/BiasVariance.html)\n",
    "    - [Guiding questions](https://github.com/justmarkham/DAT5/blob/master/homework/06_bias_variance.md) when reading this article\n",
    "- Video: [Visualizing bias and variance](http://work.caltech.edu/library/081.html) (15 minutes) by Abu-Mostafa"
   ]
  },
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   "metadata": {},
   "source": [
    "## Comments or Questions?\n",
    "\n",
    "- Email: <kevin@dataschool.io>\n",
    "- Website: http://dataschool.io\n",
    "- Twitter: [@justmarkham](https://twitter.com/justmarkham)"
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