{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "f4ad785c5ba65f0b33d8dcebf88ba9d07c141c11"
   },
   "source": [
    "## Ensemble Methods\n",
    "\n",
    "![](https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRJt5zCyHpIt9K_GbD059NYcNF3rOqSmoKq3SaCnmrBAgUisPF4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "5c24c9cb934a47072c125278ba6ef416c8beb687"
   },
   "source": [
    "* **Ensemble methods** combine several **decision trees classifiers** to produce better **predictive performance** than a single **decision tree classifier.*The main principle behind the ensemble model is that a group of weak learners come together to form a strong learner, thus increasing the accuracy of the model.*** When we ***try to predict the target variable using any machine learning technique, the main causes of difference in actual and predicted values are noise, variance, and bias.** Ensemble helps to reduce these factors (except noise, which is irreducible error).\n",
    "\n",
    "![](https://www.analyticsvidhya.com/wp-content/uploads/2015/07/variance_bias.png)\n",
    "\n",
    "* **Ensemble learning** is **Fable of blind men and elephant.** All of the blind men had their own description of the elephant. Even though each of the description was true, it would have been better to come together and discuss their undertanding before coming to final conclusion. This story perfectly describes the Ensemble learning method.\n",
    "![](https://cdn-images-1.medium.com/max/1200/1*10t9S7xvWE5Z3NEZrmHG2w.jpeg)\n",
    "\n",
    "* Ensemble methods are meta-algorithms that combine several machine learning techniques into one predictive model in order to decrease variance (bagging), bias (boosting), or improve predictions (stacking). Ensemble methods can be divided into two groups: *sequential* ensemble methods where the base learners are generated sequentially (e.g. AdaBoost) and *parallel* ensemble methods where the base learners are generated in parallel (e.g. Random Forest). The basic motivation of sequential methods is to exploit the dependence between the base learners since the overall performance can be boosted by weighing previously mislabeled examples with higher weight.  The basic motivation of parallel methods is to exploit independence between the base learners since the error can be reduced dramatically by averaging. \n",
    "\n",
    "![](https://d3ansictanv2wj.cloudfront.net/figure_12-f5c12a524e288367ef4f59526486e0ce.jpg)\n",
    "\n",
    "* Most ensemble methods use a single base learning algorithm to produce homogeneous base learners, i.e. learners of the same type leading to *homogeneous ensembles*. There are also some methods that use heterogeneous learners, i.e. learners of different types, leading to *heterogeneous ensembles*. In order for ensemble methods to be more accurate than any of its individual members the base learners have to be as accurate as possible and as diverse as possible.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "d43abb06bf43060e30c25fa5b5e858f82a669f05"
   },
   "source": [
    "### Bagging\n",
    "\n",
    "![](https://www.analyticsvidhya.com/wp-content/uploads/2015/07/bagging.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "e653f81f453e3796b4c9a0965722697fefc73ac7"
   },
   "source": [
    "* **Bagging** stands for **bootstrap aggregation.*One way to reduce the variance of an estimate is to average together multiple estimates.*** \n",
    "For example, we can train $M$ different trees $f_m$ on different subsets of the data (chosen randomly with replacement) and compute the ensemble:\n",
    "\n",
    "\\begin{equation}\n",
    "   f(x) = \\frac{1}{M}\\sum_{m=1}^{M}f_m(x) \n",
    "\\end{equation}\n",
    "\n",
    "* **Bagging uses bootstrap sampling to obtain the data subsets for training the base learners.** For **aggregating** the **outputs of base learners**, ***bagging uses voting for classification and averaging for regression.***\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_uuid": "65ec862eaa22f3166bd227d6073365e12bd774d3"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import itertools\n",
    "import numpy as np\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "plt.style.use(\"fivethirtyeight\")\n",
    "\n",
    "from sklearn import datasets\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.model_selection import cross_val_score, train_test_split\n",
    "\n",
    "from mlxtend.plotting import plot_learning_curves\n",
    "from mlxtend.plotting import plot_decision_regions\n",
    "\n",
    "np.random.seed(0)\n",
    "\n",
    "import warnings\n",
    "\n",
    "def fxn():\n",
    "    warnings.warn(\"deprecated\", DeprecationWarning)\n",
    "    warnings.warn(\"ignore\", FutureWarning)\n",
    "\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\")\n",
    "    fxn()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_uuid": "c6a3ac278920cda3d5141a85373944b5f1fb9d82"
   },
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()\n",
    "X, y = iris.data[:, 0:2], iris.target\n",
    "    \n",
    "clf1 = DecisionTreeClassifier(criterion='entropy', max_depth=1)\n",
    "clf2 = KNeighborsClassifier(n_neighbors=1)    \n",
    "\n",
    "bagging1 = BaggingClassifier(base_estimator=clf1, n_estimators=10, max_samples=0.8, max_features=0.8)\n",
    "bagging2 = BaggingClassifier(base_estimator=clf2, n_estimators=10, max_samples=0.8, max_features=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_uuid": "cc6aa513f8f385adfed4c8db69e13b084b5d37ab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.63 (+/- 0.02) [Decision Tree]\n",
      "Accuracy: 0.70 (+/- 0.02) [K-NN]\n",
      "Accuracy: 0.66 (+/- 0.02) [Bagging Tree]\n",
      "Accuracy: 0.61 (+/- 0.02) [Bagging K-NN]\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1440x864 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "label = ['Decision Tree', 'K-NN', 'Bagging Tree', 'Bagging K-NN']\n",
    "clf_list = [clf1, clf2, bagging1, bagging2]\n",
    "\n",
    "fig = plt.figure(figsize=(20, 12))\n",
    "gs = gridspec.GridSpec(2, 2)\n",
    "grid = itertools.product([0,1],repeat=2)\n",
    "\n",
    "for clf, label, grd in zip(clf_list, label, grid):        \n",
    "    scores = cross_val_score(clf, X, y, cv=3, scoring='accuracy')\n",
    "    print(\"Accuracy: %.2f (+/- %.2f) [%s]\" %(scores.mean(), scores.std(), label))\n",
    "        \n",
    "    clf.fit(X, y)\n",
    "    ax = plt.subplot(gs[grd[0], grd[1]])\n",
    "    fig = plot_decision_regions(X=X, y=y, clf=clf, legend=2)\n",
    "    plt.title(label)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "7ef7b8e25fa4893917bbfeed06dc8a1084293820"
   },
   "source": [
    "* The figure above shows the decision boundary of a decision tree and k-NN classifiers along with their bagging ensembles applied to the Iris dataset. The decision tree shows axes parallel boundaries while the $k=1$ nearest neighbors fits closely to the data points. The bagging ensembles were trained using $10$ base estimators with $0.8$ subsampling of training data and $0.8$ subsampling of features. The decision tree bagging ensemble achieved higher accuracy in comparison to k-NN bagging ensemble because k-NN are less sensitive to perturbation on training samples and therefore they are called *stable learners*. Combining stable learners is less advantageous since the ensemble will not help improve generalization performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_uuid": "6f78e77ad0dfaf5827d6832822bf27e7f959ea42"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot learning curves\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "    \n",
    "plt.figure(figsize=(20,8))\n",
    "plot_learning_curves(X_train, y_train, X_test, y_test, bagging1, print_model=False, style='ggplot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "48e98cc1d567575f8a5868deabefd73352796e3e"
   },
   "source": [
    "* The figure above shows learning curves for the bagging tree ensemble. We can see an average error of $0.3$ on the training data and a U-shaped error curve for the testing data. The smallest gap between training and test errors occurs at around $80\\%$ of the training set size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_uuid": "c2cb58b966f3091034a03184c4115a4690b5c022"
   },
   "outputs": [],
   "source": [
    "#Ensemble Size\n",
    "num_est = np.linspace(1,100,20, dtype=int)\n",
    "bg_clf_cv_mean = []\n",
    "bg_clf_cv_std = []\n",
    "for n_est in num_est:    \n",
    "    bg_clf = BaggingClassifier(base_estimator=clf1, n_estimators=n_est, max_samples=0.8, max_features=0.8)\n",
    "    scores = cross_val_score(bg_clf, X, y, cv=3, scoring='accuracy')\n",
    "    bg_clf_cv_mean.append(scores.mean())\n",
    "    bg_clf_cv_std.append(scores.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_uuid": "95df90e84b7a08d31ef92074b05f25f99863af3e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "(_, caps, _) = plt.errorbar(num_est, bg_clf_cv_mean, yerr=bg_clf_cv_std, c='red', fmt='-o', capsize=5)\n",
    "for cap in caps:\n",
    "    cap.set_markeredgewidth(1)                                                                                                                                \n",
    "plt.ylabel('Accuracy'); plt.xlabel('Ensemble Size'); plt.title('Bagging Tree Ensemble');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "11217592d4323cfe2a153647403595b9b2694b90"
   },
   "source": [
    "* The figure above shows how the test accuracy improves with the size of the ensemble. Based on cross-validation results, we can see the accuracy increases until approximately $10$ base estimators and then plateaus afterwards. Thus, adding base estimators beyond $10$ only increases computational complexity without accuracy gains for the Iris dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "78aabc15797be4e16762622f1384ce971cfc98c5"
   },
   "source": [
    "* A commonly used class of ensemble algorithms are forests of randomized trees. In **random forests**, each tree in the ensemble is built from a sample drawn with replacement (i.e. a bootstrap sample) from the training set. In addition, instead of using all the features, a random subset of features is selected further randomizing the tree. As a result, the bias of the forest increases slightly but due to averaging of less correlated trees, its variance decreases resulting in an overall better model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "14b00e292e88b73cdacceafea4fe46c9fb174e7b"
   },
   "source": [
    "* In **extremely randomized trees** algorithm randomness goes one step further: the splitting thresholds are randomized. Instead of looking for the most discriminative threshold, thresholds are drawn at random for each candidate feature and the best of these randomly-generated thresholds is picked as the splitting rule. This usually allows to reduce the variance of the model a bit more, at the expense of a slightly greater increase in bias."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "52d6434de722fa62aae3239a55954f70c882d10d"
   },
   "source": [
    "### Boosting\n",
    "\n",
    "![](https://www.analyticsvidhya.com/wp-content/uploads/2015/07/boosting.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "eb75fc3fa4d395bde3e3037c6f42b9a91f1a96cf"
   },
   "source": [
    "* **Boosting** refers to a ***family of algorithms that are able to convert weak learners to strong learners.**The main principle of boosting is to fit a sequence of weak learners (models that are only slightly better than random guessing, such as small decision trees) to weighted versions of the data, where more weight is given to examples that were mis-classified by earlier rounds.*** \n",
    "* ***The predictions are then combined through a weighted majority vote (classification) or a weighted sum (regression) to produce the final prediction.*** \n",
    "* *The principal difference between boosting and the committee methods such as bagging is that base learners are trained in sequence on a weighted version of the data.*   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_uuid": "2bb3b418692bd499d063b076907f7d759d0f4db4"
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "import numpy as np\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "from sklearn import datasets\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.model_selection import cross_val_score, train_test_split\n",
    "\n",
    "from mlxtend.plotting import plot_learning_curves\n",
    "from mlxtend.plotting import plot_decision_regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_uuid": "ed12322f85b06db9ebd7b3fc3fb0201d06efdf03"
   },
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()\n",
    "X, y = iris.data[:, 0:2], iris.target\n",
    "    \n",
    "#XOR dataset\n",
    "#X = np.random.randn(200, 2)\n",
    "#y = np.array(map(int,np.logical_xor(X[:, 0] > 0, X[:, 1] > 0)))\n",
    "    \n",
    "clf = DecisionTreeClassifier(criterion='entropy', max_depth=1)\n",
    "\n",
    "num_est = [1, 2, 3, 10]\n",
    "label = ['AdaBoost (n_est=1)', 'AdaBoost (n_est=2)', 'AdaBoost (n_est=3)', 'AdaBoost (n_est=10)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_uuid": "8a5467bb579892fbf797891882b1eb3e4403676a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x936 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20, 13))\n",
    "gs = gridspec.GridSpec(2, 2)\n",
    "grid = itertools.product([0,1],repeat=2)\n",
    "\n",
    "for n_est, label, grd in zip(num_est, label, grid):     \n",
    "    boosting = AdaBoostClassifier(base_estimator=clf, n_estimators=n_est)   \n",
    "    boosting.fit(X, y)\n",
    "    ax = plt.subplot(gs[grd[0], grd[1]])\n",
    "    fig = plot_decision_regions(X=X, y=y, clf=boosting, legend=2)\n",
    "    plt.title(label)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "1dbf80f4772c6ddb6f32ec9ce3793ae57e8d508b"
   },
   "source": [
    "* The **AdaBoost algorithm** is illustrated in the figure above. ***Each base learner consists of a decision tree with depth $1$, thus classifying the data based on a feature threshold that partitions the space into two regions separated by a linear decision surface that is parallel to one of the axes.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_uuid": "611e7d6cad25d75fe43fd620d4608e6b74fe5848"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot learning curves\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "\n",
    "boosting = AdaBoostClassifier(base_estimator=clf, n_estimators=10)\n",
    "        \n",
    "plt.figure(figsize=(20,8))\n",
    "plot_learning_curves(X_train, y_train, X_test, y_test, boosting, print_model=False, style='ggplot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_uuid": "f5e3f199600a4189fc239b71c6f8e5bdb5a9a1e2"
   },
   "outputs": [],
   "source": [
    "#Ensemble Size\n",
    "num_est = np.linspace(1,100,20, dtype=int)\n",
    "bg_clf_cv_mean = []\n",
    "bg_clf_cv_std = []\n",
    "for n_est in num_est:\n",
    "    ada_clf = AdaBoostClassifier(base_estimator=clf, n_estimators=n_est)\n",
    "    scores = cross_val_score(ada_clf, X, y, cv=3, scoring='accuracy')\n",
    "    bg_clf_cv_mean.append(scores.mean())\n",
    "    bg_clf_cv_std.append(scores.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_uuid": "21dddbf48e1b0a08dac2f6875fc3480807fe68b0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "\n",
    "(_, caps, _) = plt.errorbar(num_est, bg_clf_cv_mean, yerr=bg_clf_cv_std, c='red', fmt='-o', capsize=5)\n",
    "for cap in caps:\n",
    "    cap.set_markeredgewidth(1) \n",
    "plt.xticks(range(4), ['KNN', 'RF', 'NB', 'Adaboost'])\n",
    "plt.ylabel('Accuracy'); plt.xlabel('Ensemble Size'); plt.title('AdaBoost Ensemble');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "681555e22aaaf7573a56b7a284a76caa39de2cd1"
   },
   "source": [
    "The figure above shows how the test accuracy improves with the size of the ensemble."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "3fb2ea7fb551a95af323e8ee2ab6544748a3b150"
   },
   "source": [
    "* **Gradient Tree Boosting** is a ***generalization of boosting to arbitrary differentiable loss functions. It can be used for both regression and classification problems.***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "96d9f4e1643eead02864e49812c4e29d12fcdb4f"
   },
   "source": [
    "### Stacking\n",
    "\n",
    "![](https://cdn-images-1.medium.com/max/665/0*G0Mv1RkCPMqiPZzu.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "fe1a502107cddf6bb426ffb061a6584593ef28d5"
   },
   "source": [
    "* **Stacking is an ensemble learning technique that combines multiple classification or regression models via a meta-classifier or a meta-regressor.**\n",
    "* ***The base level models are trained based on complete training set then the meta-model is trained on the outputs of base level model as features. The base level often consists of different learning algorithms and therefore stacking ensembles are often heterogeneous.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_uuid": "f31a6cab3013bfd56d864514d0ea01b8982d0f55"
   },
   "outputs": [],
   "source": [
    "import itertools\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "from sklearn import datasets\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB \n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from mlxtend.classifier import StackingClassifier\n",
    "\n",
    "from sklearn.model_selection import cross_val_score, train_test_split\n",
    "\n",
    "from mlxtend.plotting import plot_learning_curves\n",
    "from mlxtend.plotting import plot_decision_regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_uuid": "ae9316c955673c94f2d12a61f7a9d98d3b4af7f3"
   },
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()\n",
    "X, y = iris.data[:, 1:3], iris.target\n",
    "\n",
    "clf1 = KNeighborsClassifier(n_neighbors=1)\n",
    "clf2 = RandomForestClassifier(random_state=1)\n",
    "clf3 = GaussianNB()\n",
    "lr = LogisticRegression()\n",
    "sclf = StackingClassifier(classifiers=[clf1, clf2, clf3], \n",
    "                          meta_classifier=lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_uuid": "48bdb8dcc5093483da9c99767af0d3095687d052"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.91 (+/- 0.01) [KNN]\n",
      "Accuracy: 0.93 (+/- 0.05) [Random Forest]\n",
      "Accuracy: 0.92 (+/- 0.03) [Naive Bayes]\n",
      "Accuracy: 0.95 (+/- 0.03) [Stacking Classifier]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x936 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "label = ['KNN', 'Random Forest', 'Naive Bayes', 'Stacking Classifier']\n",
    "clf_list = [clf1, clf2, clf3, sclf]\n",
    "    \n",
    "fig = plt.figure(figsize=(20,13))\n",
    "gs = gridspec.GridSpec(2, 2)\n",
    "grid = itertools.product([0,1],repeat=2)\n",
    "\n",
    "clf_cv_mean = []\n",
    "clf_cv_std = []\n",
    "for clf, label, grd in zip(clf_list, label, grid):\n",
    "        \n",
    "    scores = cross_val_score(clf, X, y, cv=3, scoring='accuracy')\n",
    "    print (\"Accuracy: %.2f (+/- %.2f) [%s]\" %(scores.mean(), scores.std(), label))\n",
    "    clf_cv_mean.append(scores.mean())\n",
    "    clf_cv_std.append(scores.std())\n",
    "        \n",
    "    clf.fit(X, y)\n",
    "    ax = plt.subplot(gs[grd[0], grd[1]])\n",
    "    fig = plot_decision_regions(X=X, y=y, clf=clf)\n",
    "    plt.title(label)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "72ef5e6dd593d64978073cb2ee2ddf586bc53ea9"
   },
   "source": [
    "* ***The stacking ensemble*** is illustrated int the figure above. It consists of ***k-NN, Random Forest and Naive Bayes base classifiers whose predictions are combined by Lostic Regression as a meta-classifier.*** We can see the blending of decision boundaries achieved by the ***stacking classifier.***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_uuid": "a48ee68f6d775485137e8a34dc572dfac92aecbf"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot classifier accuracy    \n",
    "plt.figure(figsize=(20,8))\n",
    "(_, caps, _) = plt.errorbar(range(4), clf_cv_mean, yerr=clf_cv_std, c='orange', fmt='-o', capsize=5)\n",
    "for cap in caps:\n",
    "    cap.set_markeredgewidth(1)                                                                                                                                \n",
    "plt.xticks(range(4), ['KNN', 'RF', 'NB', 'Stacking'])        \n",
    "plt.ylabel('Accuracy'); plt.xlabel('Classifier'); plt.title('Stacking Ensemble');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_uuid": "8274859ca9f60f6e8041b576c380ec846239caa4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot learning curves\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "    \n",
    "plt.figure(figsize=(20,8))\n",
    "plot_learning_curves(X_train, y_train, X_test, y_test, sclf, print_model=False, style='ggplot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "817fc4fb9d26be30d6b9d28f8ecbc39a401863e8"
   },
   "source": [
    "**We can see that stacking achieves higher accuracy than individual classifiers and based on learning curves, it shows no signs of overfitting.**"
   ]
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}