618_09_Classification.ipynb

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    "# SI 618: Classification\n",
    "\n",
    "## Objectives:\n",
    "- Be able to perform classifications using the following methods and interpret their results.\n",
    "  - Naive Bayes\n",
    "  - Decision Tree\n",
    "  - Random Forest\n",
    "  \n",
    "Note: some slides from Eytan Adar and Kevyn Collins-Thompson\n"
   ]
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   "source": [
    "### <font color=\"red\">NOTE: use conda to install the ```pydotplus``` and ```graphviz``` packages</font>"
   ]
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   "source": [
    "![](resources/classification/Slide02.png)"
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    "![](resources/classification/Slide03.png)"
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    "![](resources/classification/Slide04.png)"
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    "![](resources/classification/Slide05.png)"
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    "![](resources/classification/Slide06.png)"
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    "![](resources/classification/Slide07.png)"
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    "![](resources/classification/Slide08.png)"
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    "![](resources/classification/Slide09.png)"
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    "![](resources/classification/Slide10.png)"
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    "![](resources/classification/Slide11.png)"
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    "![](resources/classification/Slide12.png)"
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    "![](resources/classification/Slide13.png)"
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    "![](resources/classification/Slide14.png)"
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    "![](resources/classification/Slide15.png)"
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    "![](resources/classification/Slide16.png)"
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    "![](resources/classification/Slide17.png)"
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    "![](resources/classification/Slide18.png)"
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    "![](resources/classification/Slide19.png)"
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    "![](resources/classification/Slide20.png)"
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    "![](resources/classification/Slide21.png)"
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    "![](resources/classification/Slide22.png)"
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    "![](resources/classification/Slide23.png)"
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    "![](resources/classification/Slide24.png)"
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    "![](resources/classification/Slide25.png)"
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    "![](resources/classification/Slide26.png)"
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    "![](resources/classification/Slide27.png)"
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    "![](resources/classification/Slide28.png)"
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    "![](resources/classification/Slide30.png)"
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    "![](resources/classification/Slide31.png)"
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    "![](resources/classification/Slide32.png)"
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    "![](resources/classification/Slide33.png)"
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    "![](resources/classification/Slide34.png)"
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    "![](resources/classification/Slide36.png)"
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  {
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy as sp\n",
    "import sklearn as sk\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import sklearn.ensemble as skens\n",
    "import sklearn.metrics as skmetric\n",
    "import sklearn.naive_bayes as sknb\n",
    "import sklearn.tree as sktree\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set(style='white', color_codes=True, font_scale=1.3)\n",
    "import sklearn.externals.six as sksix\n",
    "import IPython.display as ipd\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn import metrics\n",
    "import os\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Filter all warnings.\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the iris dataset\n",
    "df_iris = sns.load_dataset('iris')\n",
    "df_iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split the data set into training and testing subsets using a scikit_learn function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_iris_train,df_iris_test = train_test_split(df_iris, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(105, 45)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_iris_train),len(df_iris_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Decision Trees\n",
    "\n",
    "## 1.1 Train a decision tree classifier\n",
    "\n",
    "We will train a decision tree classifier to classify iris species. Here are the specifications:\n",
    "- __Objective__: predict which species an iris instance belongs to.\n",
    "- __Possible classes__: \"setosa\", \"versicolor\", and \"virginica\"\n",
    "- __Features__: all four features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=2,\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=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Build and train the classifier\n",
    "dt_model = sktree.DecisionTreeClassifier(max_depth=2,\n",
    "                                         criterion='entropy')\n",
    "# given first 4 columns, learn the species\n",
    "dt_model.fit(df_iris_train.ix[:,:4],df_iris_train.species)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.externals.six import StringIO  \n",
    "from IPython.display import Image  \n",
    "from sklearn.tree import export_graphviz\n",
    "import pydotplus\n",
    "dot_data = StringIO()\n",
    "export_graphviz(dt_model, out_file=dot_data,  \n",
    "                filled=True, rounded=True,\n",
    "                special_characters=True)\n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To interpret the above plot, please read through the following paragraphs quoted from http://www.thegrimmscientist.com/tutorial-decision-trees/, which has slightly different results than our data set because they used alightly different data.  Their results looks like:\n",
    "![](http://www.thegrimmscientist.com/content/images/2017/06/TreeDepth1.png)\n",
    "> This image needs a bit of explanation. The top node represents the whole dataset. The whole dataset has 150 samples, as seen in the third line of that first node. We'll explain entropy more later, but for now, think of it as a formalization of the amount of uncertainty in the data. The first line on the top node gives us the decision our tree magically found. When that decision \"petal width is less than or equal to 0.80 cm\" is true for a given sample, that sample is assigned to the bottom left node. When it is false, the sample is assigned to the bottom right node.\n",
    "\n",
    ">On these two leaf nodes, the number of samples is shown on the second line. The number of samples broken down by class is on the third line. On the left leaf node, all 50 of the samples in class 1 and none from the other two classes are represented. Given the decision and the data at hand, we are certain to have a sample in class 1 (setosa). Since we are totally certain about that, uncertainty (entropy) is 0.\n",
    "\n",
    ">When our petal width <= 0.80 decider is false, the given sample is assigned to the right node. It works out that all 50 samples from class 2 (versicolor) and all 50 samples from class 3 (virginica) are assigned to this right leaf. Thus, when we're at this leaf given the decision, we're evenly unsure whether we're in class 2 or class 3. The amount of uncertainty this works out to be is 1.0. Again, we'll explain the math and meaning behind that later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"magenta\">Question 1:</font>\n",
    "1. Train a decision tree with `max_depth=2`. Plot it, if you have graphviz installed, as a graph (same below).\n",
    "2. Train a decision tree with `max_depth=3`.\n",
    "3. Train a decision tree without `max_depth` limitation.\n",
    "\n",
    "\n",
    "_NOTE_: see http://www.thegrimmscientist.com/tutorial-decision-trees/ for awesome visualizations and explanations to intuitively understand what decision tree algorithm is doing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', 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=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Build and train the classifier\n",
    "dt_model = sktree.DecisionTreeClassifier(max_depth=3,\n",
    "                                         criterion='entropy')\n",
    "# given first 4 columns, learn the species\n",
    "dt_model.fit(df_iris_train.ix[:,:4],df_iris_train.species)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.externals.six import StringIO  \n",
    "from IPython.display import Image  \n",
    "from sklearn.tree import export_graphviz\n",
    "import pydotplus\n",
    "dot_data = StringIO()\n",
    "export_graphviz(dt_model, out_file=dot_data,  \n",
    "                filled=True, rounded=True,\n",
    "                special_characters=True)\n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,\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=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Build and train the classifier\n",
    "dt_model = sktree.DecisionTreeClassifier(criterion='entropy')\n",
    "# given first 4 columns, learn the species\n",
    "dt_model.fit(df_iris_train.ix[:,:4],df_iris_train.species)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.externals.six import StringIO  \n",
    "from IPython.display import Image  \n",
    "from sklearn.tree import export_graphviz\n",
    "import pydotplus\n",
    "dot_data = StringIO()\n",
    "export_graphviz(dt_model, out_file=dot_data,  \n",
    "                filled=True, rounded=True,\n",
    "                special_characters=True)\n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Deploying a Decision Tree Classifier\n",
    "\n",
    "We train a decision tree with `max_depth=2` to cluster the irises into three species."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color =\"red\">Now use your newly created function to call max_depth=2, max_depth=3 and no limit on max_depth.  Examine the differences using comparePlot (below)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=2,\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=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first train the model with max_depth = 2 (similar to the training your did above)\n",
    "\n",
    "## Build and train the classifier\n",
    "dt_model = sktree.DecisionTreeClassifier(max_depth=2,\n",
    "                                         criterion='entropy')\n",
    "# given first 4 columns, learn the species\n",
    "dt_model.fit(df_iris_train.ix[:,:4],df_iris_train.species)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "      <th>predicted_label_tree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width     species  \\\n",
       "87            6.3          2.3           4.4          1.3  versicolor   \n",
       "40            5.0          3.5           1.3          0.3      setosa   \n",
       "103           6.3          2.9           5.6          1.8   virginica   \n",
       "28            5.2          3.4           1.4          0.2      setosa   \n",
       "99            5.7          2.8           4.1          1.3  versicolor   \n",
       "143           6.8          3.2           5.9          2.3   virginica   \n",
       "112           6.8          3.0           5.5          2.1   virginica   \n",
       "116           6.5          3.0           5.5          1.8   virginica   \n",
       "14            5.8          4.0           1.2          0.2      setosa   \n",
       "82            5.8          2.7           3.9          1.2  versicolor   \n",
       "\n",
       "    predicted_label_tree  \n",
       "87            versicolor  \n",
       "40                setosa  \n",
       "103            virginica  \n",
       "28                setosa  \n",
       "99            versicolor  \n",
       "143            virginica  \n",
       "112            virginica  \n",
       "116            virginica  \n",
       "14                setosa  \n",
       "82            versicolor  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is testing the model \n",
    "predicted_labels = dt_model.predict(df_iris_test.ix[:,:4])\n",
    "df_iris_test['predicted_label_tree'] = predicted_labels\n",
    "df_iris_test.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# utility class to compare the predictions versus ground truth\n",
    "def comparePlot(input_frame,real_column,predicted_column):\n",
    "    df_a = input_frame.copy()\n",
    "    df_b = input_frame.copy()\n",
    "    df_a['label_source'] = 'Species'\n",
    "    df_b['label_source'] = 'Classifier'\n",
    "    df_a['label'] = df_a[real_column]\n",
    "    df_b['label'] = df_b[predicted_column].apply(lambda x: 'Predict %s'%x)\n",
    "    df_c = pd.concat((df_a, df_b), axis=0, ignore_index=True)\n",
    "    sns.lmplot(x='sepal_length', y='sepal_width', col='label_source',\n",
    "               hue='label', data=df_c, fit_reg=False, size=4);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 736.98x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# see the difference\n",
    "comparePlot(df_iris_test,\"species\",\"predicted_label_tree\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"magenta\">Question 2: Now repeat the above steps with max_depth=3 and no limit on max_depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 736.98x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Build and train the classifier\n",
    "dt_model = sktree.DecisionTreeClassifier(max_depth=3,\n",
    "                                         criterion='entropy')\n",
    "# given first 4 columns, learn the species\n",
    "dt_model.fit(df_iris_train.ix[:,:4],df_iris_train.species)\n",
    "\n",
    "predicted_labels = dt_model.predict(df_iris_test.ix[:,:4])\n",
    "df_iris_test['predicted_label_tree'] = predicted_labels\n",
    "\n",
    "comparePlot(df_iris_test,\"species\",\"predicted_label_tree\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Random Forest\n",
    "\n",
    "Let's build a random forest classifier to classify irises into three species.\n",
    "- __Objective__: predict which species an iris instance belongs to.\n",
    "- __Possible classes__: \"setosa\", \"versicolor\", and \"virginica\"\n",
    "- __Features__: all four features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
       "            max_depth=None, max_features='auto', 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, n_estimators=10, n_jobs=None,\n",
       "            oob_score=True, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# build a random forest\n",
    "rf_model = skens.RandomForestClassifier(n_estimators=10,oob_score=True, criterion='entropy')\n",
    "rf_model.fit(df_iris_train.ix[:,:4],df_iris_train.species)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=\"red\">Use the same approach that you did in predicting the Decision Tree Classifier.  Note that you don't need to use .sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# now do the predicting... use the same approach as you did in testing the Decision Tree Classifier above,\n",
    "# but don't include the .sample() line\n",
    "# this is testing the model \n",
    "predicted_labels = rf_model.predict(df_iris_test.ix[:,:4])\n",
    "df_iris_test['predicted_rf_tree'] = predicted_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 736.98x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "comparePlot(df_iris_test,\"species\",\"predicted_rf_tree\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also check __feature importance__ as dumped by the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1dc1f7f0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NECSt3f0zgkODg81shpm9DtxLeHgxSX8FSoAfEvTyXgdeAzqZWV447Y+7Wa8V8M+4+orjH+/BR2EgltoM1K5AzSIikkYVCj133wqcTTCScR7QE/gHQY+smKCX0y7u1hKIvyRgR7lN5gIlZtYYeA/oDrwLjASGVbDGHcD/EYTbmcBfCIL6DILDqlvCmnen/GCV4n3sbndfVqcBLyIiVUxFz+mdBox298XuPpygd/RvgoDLA+q4e6G7FxL04u4hGLRSqiBuWy0JzoctJRj8sh04190nufsrwFFUPEBKz+udQRB4fwUaEwykeWEPA02WEZybK60vFzgxbr4Gp4iIVFMVHb1ZBIwwsw0EPb02BOf5RgD5wCwzG0RwmG8iQU9vVdz6o8xsNbCeYEDMq+6+1MyOJxgxeZ6ZrQDOA37F7ntSiVgATA7XX+Lu28MLyX9OcO5xd+4DFpvZEIJzfr9g18AuCn8WmNmSCtYlIiJZUNHDm8uAPsB1wEqCYLnZ3Z8FrgDeIQjDN4CdwDnu/k3cJmYShN1i4COCHh7A7wlGfz5GcB7tMoJeWR0zO7YCdX5MELZvxg06WcR3l1Lsbp03gJ+FbfsnwYjP+HN/7xKc03yJYPCOiIhUEzmxWGaP1oXX6fVw9/kZ3XEVFl7/t+boLsPJy6+Xln3Mm9gzLdsVEcmGtWvX0rVrV4Cj3f3DRNerVp+9KSIiUhnV8mPIzKwD330yyp60dPdPMlGPiIhUDxkPPXdPxVD+ZQQjRffm8xTsR0RE9iPVsqfn7t8ChdmuQ0REqhed0xMRkchQ6ImISGRUy8Ob+6uHRnajadOmadn29uISauZV5CNMRUT2H+rpRYQCT0REoSciIhGi0BMRkchQ6ImISGQo9EREJDIUeiIiEhkKPRERiQyFnoiIRIZCT0REIkOhJyIikaHQExGRyFDoiYhIZCj0REQkMhR6IiISGQo9ERGJDIWeiIhEhkJPREQiQ6EnIiKRodATEZHIUOiJiEhkKPRERCQyFHoiIhIZCj0REYkMhZ6IiESGQk9ERCJDoSciIpGh0BMRkchQ6MlebS8uyXYJIiIpUyPbBch3BoxdSF5+vWyXsYt5E3tmuwQRkZRRT09ERCJDoSciIpGh0BMRkchQ6ImISGQo9EREJDIUeiIiEhlV9pIFMzsTeAU42N2L9rFsc2AN0Mbd38tAbW2Bw9z9lUzvW6QivvrqK9avX09xcXG2SxFJSF5eHg0bNqRu3bop3W6VDb0q7jlgIkEoi1RpX331FZ9//jlNmjShdu3a5OTkZLskkb2KxWJs27aNdevWAaQ0+HR4s2L0V0OqjfXr19OkSRPy8/MVeFIt5OTkkJ+fT5MmTVi/fn1Kt51QT8/MBgI3AE2BVcAod3/GzOoC9wC9gB3AQuB6d18frhcDBgLXA82ARcDV7r4unH8KcBfQgSCA3wZ+4e5LK9OoBOvqCwwBDHgXGOzufw/nNwMeBDoBnwDjgWnunmNmrwJHAZPNrD0wOtzteWb2FNAc+DvQz93XVKYdIqlQXFxM7dq1s12GSNJq166d8kPy++zpmVkBcD9wI9ASeBSYY2YNgYcI/sh3DW8HAc+bWfy/k3cAo4BTw/nPmlmOmR0MLADeANoAHYFcgrCqrETqGguMBE4DioFpYXtrAC8AO4FTgKHAmLj1egFrgRHA/4ubPiC8/Qg4DPhNCtohkhLq4Ul1lI73bSKHN5sDMeBDd/8IuBvoATQALgH6uPs77r4MuBQ4CTg9bv1J7j43nH8l0D681QHuBEa6+wfu/jYwEzihMg0ysxYJ1nW/uy8Ie5XjgXZmlgd0AY4FrnD399x9HnBr6UruvgkoAb529//EbW+Eu/813N5DQEFl2iEiIqmXyOHNFwkOS75lZsuBecB0gsOCAO+bWfzyNcJ5r4WPF5fOcPfVZrYJaO3uS8xsOjDYzE4M1zkJ2OtIzQQcn2Bdq+LmfRX+rAm0JQj4L+Lmv5HAflfH3d8M6HiSVGnbi0uomZdbpffZpUuXssEM8WrWrMm7775b6Xq2bNnCCy+8wCWXXFLpbSWqT58+tG7dmptuuilj+9ybWCzGU089Rc+ePalVq1a2y0m7fYaeu281s7MJDj+eD/QEfklwyK+YoEcTK7dafGDsKDcvFygxs8YE5/DeA/4EPAK0Am5Lvhm7qJFgXdt3s25OuG5FBviU/w4eHU+SKq1mXi49hj2X0X1W5Fs7hg4dSq9evXaZlqrDXjNmzODll1/OaOhVNUuWLGHUqFGcf/75kQi9RM7pnQaMdvfF7j6cIJj+DbQD8oA67l7o7oXAJoJzcs3iNlEQt62WwA+ApQTnxrYD57r7JHd/hWCASGXfzSsSrGtP3gOamVmDuGkdyi1TPkxFJE3q1KlDgwYNdrnVr18/JduOxfSrHLXnIJEeTREwwsx+FV6IfQHBeb4ngeeBWWZ2upmdADxOcE4u/tDhKDM7x8zaATOAV8PzXhuBwwlGPTYPR4j+CqjUvxru7gnWtScvEwTnDDNrbWbnAreXW6YIMDM7rDK1ikjlvfLKK/To0YO2bdvSo0cP5s+fXzZvx44djB8/njPPPJMTTjiBjh07MmnSJACefvpppkyZwr/+9S9KT4V06dKFxx57rGz9tWvXYma8//77ZfPHjx9P586d6dKlC1u2bOHzzz9n8ODBFBQU0KlTJ0aPHs2WLVsSqr10+4sWLeKcc87hxBNPZMiQIXzyySdcc801nHjiiVx44YWsWLFil+Wfe+45unTpwkknncSwYcMoKvrurNDq1au59tprad++PT/60Y8YM2YM33zzDQBvvvkmp59+OnfeeScnn3wy1113HX379gXgpJNO4s0339zrcwYwfPhwxowZw0033URBQQFdunRh2rRpZfN37tzJ/fffT+fOnSkoKODKK69kzZrvBrL/4Q9/KGtr7969ef311xN6rlJln6EXDgTpA1wHrAQmAze7+7PAFcA7BOf53iAY8XiOu38Tt4mZBKM/FwMfEfTwAH5PMODjMeCfwGUElzfUMbNjK9muROraLXePhTXWBt4iaO9D7Ho4dArQD3i4knWKSCW4O9dffz19+/Zl/vz5XHXVVdxyyy0sWrQIgAcffJAFCxYwYcIEXnzxRQYNGsS0adNYsmQJ3bt3p3///hx33HG89tpr+9jTd+bOncv999/P5MmTyc/P55e//CU1atTgqaeeYsqUKaxcuZIRI0Yk1Y57772Xe+65h6lTp7Jw4UJ69+5N9+7deeqpp6hduzZ3333395a/4447mDlzJitXruR//ud/APjyyy+5/PLLyc/P5/HHH2fSpEm88sorjB07tmzdDRs28Omnn/LMM89www03MHnyZABeeuklCgoK9vqclXryySdp0qQJTz/9NBdffDGTJk1i5cqVAEyZMoVHH32UUaNG8cwzz3DIIYcwcOBAYrEYixYt4q677mLo0KE8//zz9OzZk2uvvZagr5IZCV2n5+5zgDm7mb6ZYETm3rzu7t97B7j7ToKe3a/KzZoV/lxFgoc63f3D+GX3VZe755R7/Grp+uGlGK3cvWvpfDO7BFgXt/w0wkscQuW3N5Mg7EWkksaNG8fEiRN3mTZr1izatm3L9OnT6dmzZ9k5uWbNmvHBBx8wY8YMOnfuTMuWLRk3bhzt27cH4NJLL2Xq1KmsWrWKDh06kJ+fT25uLg0aNPjefvfk/PPPp3Xr1gC88cYbFBYWMnv2bGrWrAnAnXfeyXnnncdnn31Go0aNEtrmwIEDOeGEYOD6cccdR6NGjbjooosA6NmzJw888MAuyw8dOpTTTjsNgJtvvpkrr7ySDRs2sGDBgrLnrFatWrRs2ZJbb72VgQMHMmTIkLL1r776apo1C872fPFFMNShXr161KxZc5/PWenzPHjwYAAGDRrErFmzWLZsGWbGE088waBBgzj77LMBuPXWW5k2bRpff/01Dz74IFdddRXnnXceAH379mXp0qU88sgjuwRzOuljyL4vBsw1s5HAH4AmBBegfy/0RST9rr32Wi688MJdpjVu3BiAwsJC3n//febNm1c2b8eOHdSrVw+As88+m7/97W/cddddrFmzhpUrV/LFF1+wc+fOCtfTpEmTsvurV69m27ZtnHLKKd9bbs2aNQmH3pFHHll2v3bt2rvs48ADD2T79l3H3ZUGEkCbNm2IxWIUFhZSWFjIcccdt8uAlJNPPpmdO3fywQcf7LYN5SXynJUGZqk6depQXFzMl19+yaZNm2jTpk3ZvEMOOaRspGphYSHLli3jt7/9bdn84uJi2rZtu+cnJ8WqfOiZWQf2/RmXLd39k1Tsz92/MLOLCS6qv4NgEMyjxF2rJyKZc+ihh3LUUUftdl5JSQl9+vTh5z//+S7TDzggOHMzefJkHnvsMXr16kX37t0ZOXIkl19+ecL7LikpPyibXQJlx44dHHHEEcyYMeN7yyXTe8zN3fUyjtL6E1m+NIxyc3OpVavW90a2lrYhfsDK3kZpJvKclfZq48ViMfLy8vZad0lJCcOGDeOss87a5/bSJa2hV/4wYgUtIxgpujefp2A/Zdx9PjB/nwuKSFa1aNGCjz76aJdQnDVrFps2bWLIkCHMnj2bG2+8kd69ewPBh29v3LixLADKB0ReXt4ug1A+/vjjfe5//fr11KlTp2xE6erVq5kwYQK33XYb+fn5KWlnecuXL6dz584ALFu2jNzcXI455hg++OAD5s2bx7ffflsWbP/4xz/IycmhefPmu/T2SpV/Dvb1nO3NwQcfzGGHHcby5cspKAgG7hcVFdGtWzdmzZpFixYtWLdu3S6v16RJk6hfv37ZgJp0q/I9PXf/FijMdh0iUvX079+fn/70p0ybNo3zzjuPpUuXMmHCBEaOHAkEh9YWLVpEhw4d2Lx5M5MmTaK4uLjscGF+fj4bN27k448/5sgjj6RNmzY8++yzdO7cmW+//Zbf/OY3e70m8PTTT+fYY49l6NCh3HTTTcRiMW655RZq1qxJw4YN09bucePGUbduXWKxGHfccQc9e/bk0EMPpUePHkyZMoXhw4czaNAgNmzYwO2330737t1p0KDBbkOvNJhXrFhBmzZt9vmc7Uu/fv144IEHaNq0Kc2aNeO+++6jfv36HHPMMQwYMIChQ4dyzDHHcOqpp/Lyyy/zu9/9bpfRn+lW5UNPRNJje3FJhS4Wr+w+U/kpMK1bt+bee+/lvvvuY/LkyTRs2JBhw4aVHe4cN24co0eP5oILLqB+/fr8+Mc/pm7duixfvhyAc845hzlz5nDBBRewcOFChgwZwsiRI7nkkks44ogjGDFiBNddd90e93/AAQcwdepUxo4dy+WXX05eXh4dO3ZMevRmsnr06MHgwYP55ptv6NGjR9k5s/z8fKZPn87YsWPp3bs3Bx98MBdeeCHXX3/9HrfVsmVLOnbsSL9+/Rg/fvw+n7N96d+/P0VFRYwYMYKtW7fSvn17pk6dygEHHEC3bt24+eabmT59OmPGjOHII4/k7rvv5owzzkjJ85KInKhdmFgVlX4R7dFdhpOXXy/b5ewi038UJfVWrFjB8ccfv+8Fpcpbu3YtXbt2Zd68ebRs2TLb5WTEnt6/pc8FcHQ4gj8h+j49ERGJDB3erEIeGtmNpk2bZruMXWTjQ4lFRNJFoSd7pcATqTqaNm2a0U8v2R/p8KaIiESGQk8kAjRgTaqjdLxvFXoi+7m8vDy2bduW7TJEkrZt27Z9fspLshR6Ivu5hg0bsm7dOrZu3aoen1QLsViMrVu3sm7dupRf5K+BLCL7ubp16wLwySefUFxcnOVqRBKTl5fH4YcfXvb+TRWFnkgE1K1bN+V/PESqIx3eFBGRyFDoiYhIZCj0REQkMhR6IiISGRrIUjXkAnz22WfZrkNEpFqI+3uZ1GclKvSqhsYAl112WbbrEBGpbhoDqxNdWKFXNSwBOgGfAiVZrkVEpDrIJQi8JcmspC+RFRGRyNBAFhERiQyFnoiIRIZCT0REIkOhJyIikaHQExGRyFDoiYhIZCj0REQkMnRxegaYWR5wD/DfQAx4CBjh7jt3s+wPgAeA84EiYKK7T8pguSmRTJvj1jkMWA50dff3MlJoiiT5GrcCJgE/Ar4Gfg9x/XwyAAAE+ElEQVTc7O7bMldx5STZ3pOA3wAnA18AU9397gyWmxIVeU+H6z0CNHP3M9NeZAol+RoPBSaWm/xHd78g7YUmSaGXGXcC3YDuQF3gEWAzMG43y04n+JSBTkBLYIaZfeLuczJUa6ok0+bSwJsPNMxUgSmWUHvN7GBgAbCIIPQaE7zmNYFfZbDeykq0vYcALwKPA1cCrYDZZrbR3adntOLKS+o9DWBmFwB9CF7v6iaZ9rYieB/fHDftm3QXWBH6RJY0M7MDgY3Az9x9fjjtCuAu4Ij4/5rM7ChgDdC2tKdjZrcC57v7DzNefAUl0+ZwXjeCX5iNQDugTXXq6SX5GvcCHgYOd/dvw2mXAVPc/dCMF18BSba3LXAj0Ld0upk9DRS5e9+MF19Byb6nw/mHAsuAj4Ad1amnV4Hf4TeA2e4+JePFJknn9NKvHZAPLI6b9hfgcKBFuWVPBTaX+4P/F+Dk8E1YXSTTZoBzgXuBi9NfWlok0943gYtKAy8UA2qaWXX5fUy4ve6+zN0vd/edZpZjZp2AzsBLGas2NZJ9T0Pwnp4L/C29paVFsu1tBXgG6qo0Hd5MvybAFnf/T9y00u/EaAqsKrfsJ+XW/4zgn5PGBL3A6iCZNuPuvwYws+YZqS71Em6vu68D1pU+NrNcYDDw132dG6pCknp943wJ/IDgMPbj6SsvLZJqs5n1AE4D2gK3Z6TC1Eq4vWZ2JMHhz0vNbBqwE3gKGF3un7sqobr8Z1md5QPlX/jSx7UqsWxVtr+0I1GVae9k4ETghlQXlUZJtzfsxZ4NXAQUAFPTVl16JNzm8LDmb4EB7r41A7WlQzKvcavw5yaC13c40Jegp1vlqKeXftv4/puk9HH5X4hklq3K9pd2JCrp9oY9vKlAf+Bid1+avvJSLun2hr3Yt4C3zKwW8LiZ/drdv0pfmSmVTJvvA55391fTXVQaJdxed3/RzBq4+4Zw0jIzA5hjZte7e5Ua0KKeXvqtBeqY2UFx0xqHP9ftZtlG5aY1BnYA69NTXlok0+b9QVLtDYeCzwGuAHq7+3PpLzGlEm6vmbUwsx+XW/89gu9Cq5e+ElMumdf4cuAKMysysyKCw9edwsfNMlBrKiT1no4LvFLLCV7j8n/Psk6hl35LCf4z6hg3rRPwubuX/7bfN4DDzOy4csu+U9X+W9qHZNq8P0i2vdMJrsPs4e7PZ6C+VEumvT8Cfm9mdeKmdSC4PvHfaa0ytZJp87EE5/LahbdHCHq57fj+OfuqKuH2mtkvzKzQzHLiJhcQXGe8Nu2VJkmXLGSAmd0H/JjgP/vawGPAb9z9LjOrB5SUnjA2s+cJrlW7DvgvYCbBuYEns1F7RSXT5rh1mhMM1qlWlyxA4u0Nr9uaR3BN3txym/nc3avFL2QS7T2IoGe3hOAaLgN+B0xw9/HZqb5iKvKeDtebALSvTpcsQFKvcQuCkJxBcB7veOBBgstwxman+j3TOb3MuBE4EPgTwQWb04HST6R4GvgQ6Bc+7kfwhvkrwYnhUdUt8ELJtHl/kGh7fxpOmxze4h1M8N9xdZBQe929yMzOITjP9RbwH4I/jBMyXXAK6D29+9d4dXgI+y6C8PuS4Hz1/2a64ESopyciIpGhc3oiIhIZCj0REYkMhZ6IiESGQk9ERCJDoSciIpGh0BMRkchQ6ImISGQo9EREJDIUeiIiEhn/H23PVewwzSgbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check feature importance\n",
    "feat_importance = rf_model.feature_importances_\n",
    "pd.DataFrame({'Feature Importance':feat_importance},\n",
    "            index=df_iris_train.columns[:4]).plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuning the model\n",
    "The model paramters are called _hyperparameters_.  Setting them to appropriate values and running the model experiment again can be time-consuming, so we have a technique called cross-validation to help with this.\n",
    "\n",
    "From http://scikit-learn.org/stable/modules/cross_validation.html:\n",
    "\n",
    "> When evaluating different settings (“hyperparameters”) for estimators, such as the C setting that must be manually set for an SVM, there is still a risk of overfitting on the test set because the parameters can be tweaked until the estimator performs optimally. This way, knowledge about the test set can “leak” into the model and evaluation metrics no longer report on generalization performance. To solve this problem, yet another part of the dataset can be held out as a so-called “validation set”: training proceeds on the training set, after which evaluation is done on the validation set, and when the experiment seems to be successful, final evaluation can be done on the test set.\n",
    "\n",
    "> However, by partitioning the available data into three sets, we drastically reduce the number of samples which can be used for learning the model, and the results can depend on a particular random choice for the pair of (train, validation) sets.\n",
    "\n",
    "> A solution to this problem is a procedure called cross-validation (CV for short). A test set should still be held out for final evaluation, but the validation set is no longer needed when doing CV. In the basic approach, called k-fold CV, the training set is split into k smaller sets (other approaches are described below, but generally follow the same principles). The following procedure is followed for each of the k “folds”:\n",
    "\n",
    "> A model is trained using  of the folds as training data;\n",
    "the resulting model is validated on the remaining part of the data (i.e., it is used as a test set to compute a performance measure such as accuracy).\n",
    "The performance measure reported by k-fold cross-validation is then the average of the values computed in the loop. This approach can be computationally expensive, but does not waste too much data (as is the case when fixing an arbitrary validation set), which is a major advantage in problems such as inverse inference where the number of samples is very small."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To do this, we first set up a parameter grid with the lists of paramters we want to try:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "                 'n_estimators': [5, 10, 15, 20, 25],\n",
    "                 'max_depth': [2, 5, 7, 9],\n",
    "             }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now run the cross-validation on the classifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise-deprecating',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
       "            max_depth=None, max_features='auto', 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, n_estimators=10, n_jobs=None,\n",
       "            oob_score=True, random_state=None, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'n_estimators': [5, 10, 15, 20, 25], 'max_depth': [2, 5, 7, 9]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_clf = GridSearchCV(rf_model, param_grid, cv=10)\n",
    "grid_clf.fit(df_iris_train.ix[:,:4],df_iris_train.species)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and extract the best model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
       "            max_depth=5, max_features='auto', 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, n_estimators=25, n_jobs=None,\n",
       "            oob_score=True, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 5, 'n_estimators': 25}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_clf.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In case we want the details of the accuracy of each setting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([0.00593741, 0.00777659, 0.01176982, 0.01357851, 0.01822155,\n",
       "        0.00479212, 0.00807571, 0.01158531, 0.01541073, 0.01909678,\n",
       "        0.00568216, 0.00935433, 0.01187251, 0.01495006, 0.01922729,\n",
       "        0.00512123, 0.00835514, 0.01200347, 0.01582711, 0.01838651]),\n",
       " 'std_fit_time': array([0.00160212, 0.00116937, 0.00144785, 0.00137087, 0.00145542,\n",
       "        0.00051348, 0.00058112, 0.00114235, 0.00107063, 0.00187201,\n",
       "        0.00047241, 0.00075798, 0.00042625, 0.00153135, 0.00098517,\n",
       "        0.00039885, 0.00084964, 0.00123363, 0.00099628, 0.0012187 ]),\n",
       " 'mean_score_time': array([0.00137684, 0.00136535, 0.00187204, 0.00198734, 0.00258398,\n",
       "        0.00126565, 0.00147929, 0.00167711, 0.00211101, 0.00247734,\n",
       "        0.00139627, 0.00168095, 0.00183609, 0.00211432, 0.00244777,\n",
       "        0.001177  , 0.00160642, 0.00187292, 0.00210261, 0.00258925]),\n",
       " 'std_score_time': array([0.00039631, 0.00019308, 0.00043686, 0.00028944, 0.00044943,\n",
       "        0.00029614, 0.00019981, 0.00013878, 0.00025684, 0.00036204,\n",
       "        0.00018757, 0.00021136, 0.0001752 , 0.00039516, 0.0002381 ,\n",
       "        0.00016949, 0.00018647, 0.00028633, 0.00021966, 0.00058718]),\n",
       " 'param_max_depth': masked_array(data=[2, 2, 2, 2, 2, 5, 5, 5, 5, 5, 7, 7, 7, 7, 7, 9, 9, 9,\n",
       "                    9, 9],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_n_estimators': masked_array(data=[5, 10, 15, 20, 25, 5, 10, 15, 20, 25, 5, 10, 15, 20,\n",
       "                    25, 5, 10, 15, 20, 25],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'max_depth': 2, 'n_estimators': 5},\n",
       "  {'max_depth': 2, 'n_estimators': 10},\n",
       "  {'max_depth': 2, 'n_estimators': 15},\n",
       "  {'max_depth': 2, 'n_estimators': 20},\n",
       "  {'max_depth': 2, 'n_estimators': 25},\n",
       "  {'max_depth': 5, 'n_estimators': 5},\n",
       "  {'max_depth': 5, 'n_estimators': 10},\n",
       "  {'max_depth': 5, 'n_estimators': 15},\n",
       "  {'max_depth': 5, 'n_estimators': 20},\n",
       "  {'max_depth': 5, 'n_estimators': 25},\n",
       "  {'max_depth': 7, 'n_estimators': 5},\n",
       "  {'max_depth': 7, 'n_estimators': 10},\n",
       "  {'max_depth': 7, 'n_estimators': 15},\n",
       "  {'max_depth': 7, 'n_estimators': 20},\n",
       "  {'max_depth': 7, 'n_estimators': 25},\n",
       "  {'max_depth': 9, 'n_estimators': 5},\n",
       "  {'max_depth': 9, 'n_estimators': 10},\n",
       "  {'max_depth': 9, 'n_estimators': 15},\n",
       "  {'max_depth': 9, 'n_estimators': 20},\n",
       "  {'max_depth': 9, 'n_estimators': 25}],\n",
       " 'split0_test_score': array([0.75      , 0.83333333, 0.75      , 0.75      , 0.75      ,\n",
       "        0.75      , 0.75      , 0.75      , 0.83333333, 0.83333333,\n",
       "        0.83333333, 0.83333333, 0.83333333, 0.83333333, 0.75      ,\n",
       "        0.75      , 0.83333333, 0.83333333, 0.75      , 0.75      ]),\n",
       " 'split1_test_score': array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "        1., 1., 1.]),\n",
       " 'split2_test_score': array([0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.91666667,\n",
       "        0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.91666667,\n",
       "        0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.91666667,\n",
       "        0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.91666667]),\n",
       " 'split3_test_score': array([1.        , 0.90909091, 0.90909091, 0.90909091, 1.        ,\n",
       "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 1.        , 1.        , 1.        ]),\n",
       " 'split4_test_score': array([0.90909091, 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 1.        , 1.        , 1.        ]),\n",
       " 'split5_test_score': array([0.9, 0.9, 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. , 1. ,\n",
       "        1. , 1. , 1. , 1. , 1. , 1. , 1. ]),\n",
       " 'split6_test_score': array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "        1., 1., 1.]),\n",
       " 'split7_test_score': array([0.77777778, 0.88888889, 0.88888889, 0.88888889, 0.88888889,\n",
       "        0.88888889, 0.77777778, 1.        , 0.88888889, 0.88888889,\n",
       "        0.88888889, 0.88888889, 0.88888889, 0.88888889, 0.88888889,\n",
       "        1.        , 0.88888889, 0.88888889, 1.        , 0.88888889]),\n",
       " 'split8_test_score': array([1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 0.88888889, 0.88888889, 1.        ,\n",
       "        0.88888889, 1.        , 0.88888889, 1.        , 1.        ,\n",
       "        1.        , 0.88888889, 1.        , 1.        , 1.        ]),\n",
       " 'split9_test_score': array([0.77777778, 0.88888889, 0.88888889, 0.77777778, 0.88888889,\n",
       "        0.77777778, 0.88888889, 0.88888889, 0.88888889, 0.88888889,\n",
       "        0.88888889, 0.88888889, 0.88888889, 0.88888889, 0.88888889,\n",
       "        0.77777778, 0.88888889, 0.88888889, 0.88888889, 0.88888889]),\n",
       " 'mean_test_score': array([0.9047619 , 0.93333333, 0.93333333, 0.92380952, 0.94285714,\n",
       "        0.93333333, 0.93333333, 0.94285714, 0.94285714, 0.95238095,\n",
       "        0.94285714, 0.95238095, 0.94285714, 0.95238095, 0.94285714,\n",
       "        0.94285714, 0.94285714, 0.95238095, 0.95238095, 0.94285714]),\n",
       " 'std_test_score': array([0.09565565, 0.05880811, 0.08046283, 0.09104024, 0.08238692,\n",
       "        0.09371803, 0.09371803, 0.08238692, 0.06226998, 0.06178254,\n",
       "        0.06226998, 0.06178254, 0.06226998, 0.06178254, 0.08238692,\n",
       "        0.09436104, 0.06226998, 0.06178254, 0.08201912, 0.08238692]),\n",
       " 'rank_test_score': array([20, 15, 15, 19,  6, 15, 15,  6,  6,  1,  6,  1,  6,  1,  6,  6,  6,\n",
       "         1,  1,  6], dtype=int32),\n",
       " 'split0_train_score': array([0.97849462, 0.96774194, 0.97849462, 0.97849462, 0.97849462,\n",
       "        1.        , 0.98924731, 1.        , 1.        , 1.        ,\n",
       "        1.        , 0.98924731, 1.        , 1.        , 0.98924731,\n",
       "        1.        , 1.        , 1.        , 1.        , 1.        ]),\n",
       " 'split1_train_score': array([0.93548387, 0.95698925, 0.96774194, 0.95698925, 0.96774194,\n",
       "        0.97849462, 0.98924731, 1.        , 1.        , 1.        ,\n",
       "        1.        , 0.97849462, 1.        , 1.        , 1.        ,\n",
       "        0.98924731, 0.98924731, 0.98924731, 1.        , 0.98924731]),\n",
       " 'split2_train_score': array([0.95698925, 0.97849462, 0.97849462, 0.97849462, 0.96774194,\n",
       "        1.        , 1.        , 0.98924731, 1.        , 0.98924731,\n",
       "        0.98924731, 0.98924731, 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 1.        , 1.        , 1.        ]),\n",
       " 'split3_train_score': array([0.96808511, 0.95744681, 0.95744681, 0.95744681, 0.95744681,\n",
       "        0.9787234 , 0.9893617 , 0.9893617 , 1.        , 1.        ,\n",
       "        0.9893617 , 0.9787234 , 0.9893617 , 1.        , 1.        ,\n",
       "        0.9893617 , 1.        , 1.        , 1.        , 1.        ]),\n",
       " 'split4_train_score': array([0.93617021, 0.95744681, 0.95744681, 0.95744681, 0.95744681,\n",
       "        0.9787234 , 0.9893617 , 1.        , 0.9893617 , 0.9893617 ,\n",
       "        0.9787234 , 1.        , 0.9893617 , 1.        , 1.        ,\n",
       "        1.        , 1.        , 1.        , 1.        , 1.        ]),\n",
       " 'split5_train_score': array([0.94736842, 0.94736842, 0.94736842, 0.95789474, 0.94736842,\n",
       "        1.        , 1.        , 1.        , 1.        , 0.98947368,\n",
       "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "        0.97894737, 1.        , 0.98947368, 1.        , 1.        ]),\n",
       " 'split6_train_score': array([0.93684211, 0.95789474, 0.94736842, 0.94736842, 0.96842105,\n",
       "        0.98947368, 0.97894737, 0.98947368, 1.        , 1.        ,\n",
       "        0.98947368, 1.        , 0.98947368, 1.        , 1.        ,\n",
       "        0.98947368, 0.98947368, 0.98947368, 1.        , 0.98947368]),\n",
       " 'split7_train_score': array([0.96875   , 0.96875   , 0.96875   , 0.95833333, 0.96875   ,\n",
       "        1.        , 0.98958333, 0.98958333, 1.        , 1.        ,\n",
       "        1.        , 1.        , 0.98958333, 0.98958333, 0.98958333,\n",
       "        0.97916667, 1.        , 0.98958333, 1.        , 1.        ]),\n",
       " 'split8_train_score': array([0.96875   , 0.94791667, 0.96875   , 0.94791667, 0.96875   ,\n",
       "        0.96875   , 1.        , 1.        , 1.        , 1.        ,\n",
       "        0.98958333, 0.97916667, 0.98958333, 1.        , 1.        ,\n",
       "        0.97916667, 1.        , 1.        , 1.        , 1.        ]),\n",
       " 'split9_train_score': array([0.95833333, 0.95833333, 0.97916667, 0.98958333, 0.95833333,\n",
       "        0.98958333, 1.        , 0.98958333, 1.        , 1.        ,\n",
       "        0.97916667, 1.        , 1.        , 1.        , 1.        ,\n",
       "        0.98958333, 1.        , 1.        , 1.        , 1.        ]),\n",
       " 'mean_train_score': array([0.95552669, 0.95983826, 0.96510283, 0.96299686, 0.96404949,\n",
       "        0.98837484, 0.99257487, 0.99472494, 0.99893617, 0.99680827,\n",
       "        0.99155561, 0.99148793, 0.99473638, 0.99895833, 0.99788306,\n",
       "        0.98949467, 0.9978721 , 0.9957778 , 1.        , 0.9978721 ]),\n",
       " 'std_train_score': array([0.01495669, 0.00901757, 0.01155979, 0.01343257, 0.00835077,\n",
       "        0.01101044, 0.00676744, 0.00527587, 0.00319149, 0.00487571,\n",
       "        0.0078851 , 0.00925143, 0.00526409, 0.003125  , 0.00423454,\n",
       "        0.00809786, 0.0042561 , 0.00517169, 0.        , 0.0042561 ])}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_clf.cv_results_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Naive Bayes\n",
    "\n",
    "## 3.1 Train a Naive Bayes Classifier\n",
    "\n",
    "We will first explore using Gaussian Naive Bayes model to classify irises into either \"setosa\" or \"not setosa\".\n",
    "\n",
    "When you train a classifier, you need to think about the following three basic questions:\n",
    "- What is the prediction objective?\n",
    "- What are the possible classes?\n",
    "- What are the features?\n",
    "\n",
    "In this example, the answers to the above questions are:\n",
    "- To predict whether an iris instance is setosa or not.\n",
    "- Two possible classes: \"setosa\" and \"not setosa\".\n",
    "- Features: \n",
    "  - petal_length only (for the demonstration)\n",
    "  - petal_length and petal_width (for the exercise)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"magenta\">Question 3:</font>\n",
    "Construct the class labels\n",
    "create a new column called 'is_setosa' that's set to 'setosa' if the species is setosa and 'not_setosa' \n",
    "if it's not.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "      <th>is_setosa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>versicolor</td>\n",
       "      <td>not_setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "      <td>not_setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width     species  \\\n",
       "79            5.7          2.6           3.5          1.0  versicolor   \n",
       "109           7.2          3.6           6.1          2.5   virginica   \n",
       "\n",
       "      is_setosa  \n",
       "79   not_setosa  \n",
       "109  not_setosa  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your code here\n",
    "df_iris['is_setosa'] = np.where(df_iris.species == 'setosa','setosa', 'not_setosa' )\n",
    "df_iris_train, df_iris_test = train_test_split(df_iris, test_size = 0.3)\n",
    "df_iris.sample(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the class labels ready, let's build a Naive Bayes model. We start with a model that takes only one feature, sepal_length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None, var_smoothing=1e-09)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train the model\n",
    "gnb_model = sknb.GaussianNB()\n",
    "# given sepal length, predict if setosa\n",
    "gnb_model.fit(df_iris_train[['sepal_length']],df_iris_train['is_setosa'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we use the model to make predictions on the test dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# test the model\n",
    "y_pred = gnb_model.predict(df_iris_test[['sepal_length']])\n",
    "df_iris_test['predicted_nb'] = y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 745.605x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "comparePlot(df_iris_test,\"is_setosa\",\"predicted_nb\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"magenta\">Question 4: Repeat the above steps using petal_length and petal_width as the features.</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 745.605x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# train the model\n",
    "gnb_model = sknb.GaussianNB()\n",
    "# given sepal length, predict if setosa\n",
    "gnb_model.fit(df_iris_train[['sepal_width']],df_iris_train['is_setosa'])\n",
    "\n",
    "# test the model\n",
    "y_pred = gnb_model.predict(df_iris_test[['sepal_width']])\n",
    "df_iris_test['predicted_nb'] = y_pred\n",
    "comparePlot(df_iris_test,\"is_setosa\",\"predicted_nb\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BREAK"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color=\"magenta\">Question 5: On Your Own</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit (variable y).\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://github.com/madmashup/targeted-marketing-predictive-engine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Input variables\n",
    "\n",
    "* age (numeric)\n",
    "* job : type of job (categorical: “admin”, “blue-collar”, “entrepreneur”, “housemaid”, “management”, “retired”, “self-employed”, “services”, “student”, “technician”, “unemployed”, “unknown”)\n",
    "* marital : marital status (categorical: “divorced”, “married”, “single”, “unknown”)\n",
    "* education (categorical: “basic.4y”, “basic.6y”, “basic.9y”, “high.school”, “illiterate”, “professional.course”, “university.degree”, “unknown”)\n",
    "* default: has credit in default? (categorical: “no”, “yes”, “unknown”)\n",
    "* housing: has housing loan? (categorical: “no”, “yes”, “unknown”)\n",
    "* loan: has personal loan? (categorical: “no”, “yes”, “unknown”)\n",
    "* contact: contact communication type (categorical: “cellular”, “telephone”)\n",
    "* month: last contact month of year (categorical: “jan”, “feb”, “mar”, …, “nov”, “dec”)\n",
    "* day_of_week: last contact day of the week (categorical: “mon”, “tue”, “wed”, “thu”, “fri”)\n",
    "* duration: last contact duration, in seconds (numeric). **Important note: this attribute highly affects the output target (e.g., if duration=0 then y=’no’). The duration is not known before a call is performed, also, after the end of the call, y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model**\n",
    "* campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)\n",
    "* pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted)\n",
    "* previous: number of contacts performed before this campaign and for this client (numeric)\n",
    "* poutcome: outcome of the previous marketing campaign (categorical: “failure”, “nonexistent”, “success”)\n",
    "* emp.var.rate: employment variation rate — (numeric)\n",
    "* cons.price.idx: consumer price index — (numeric)\n",
    "* cons.conf.idx: consumer confidence index — (numeric)\n",
    "* euribor3m: euribor 3 month rate — (numeric)\n",
    "* nr.employed: number of employees — (numeric)\n",
    "\n",
    "    Predict variable (desired target):\n",
    "\n",
    "y — has the client subscribed a term deposit? (binary: “1”, means “Yes”, “0” means “No”)\n",
    "m\n",
    "The education column of the dataset has many categories and we need to reduce the categories for a better modelling. The education column has the following categories:\n",
    "\n",
    "\n",
    "Let us group “basic.4y”, “basic.9y” and “basic.6y” together and call them “basic”.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a list of tasks that you need to complete:\n",
    "\n",
    "1. Read the 'data/banking.csv' file into a dataframe and drop the \"duration\" column (see above)\n",
    "\n",
    "2. The education column has multiple entries that are very similar. Let's group “basic.4y”, “basic.9y” and “basic.6y” together and call them “basic”.\n",
    "\n",
    "2. Use get_dummies() to \"one-hot encode\" the categorical variables (think carefully about how you handle drop_first).\n",
    "\n",
    "1. Split the dataset into training and testing datasets.  We used 70-30 in the above examples; you might want to use the same or something different.  \n",
    "\n",
    "2. Create a random forest classifier for ```y```.  Use all the features in the dataset. Use 10 decision trees and use 'entropy' as the criterion.  Fit the model to your training data set.\n",
    "\n",
    "3. Add a column of predicted labels to the test dataset based on your predictions from the random forest classifier you fitted in the previous step.\n",
    "\n",
    "4. Report the accuracy of your model.\n",
    "\n",
    "5. Extract the feature importances from your model and plot them in a bar chart.  Identify the most important features in your model.\n",
    "\n",
    "6. Tune your random forest model by varying the number of decision trees (n_estimators) and tree depth (max_depth).  Report the best hyperparameters and the accuracy obtained by using those hyperparameters.\n",
    "\n",
    "7. Compare the results of your best random forest model with a (Gaussian) Naive Bayes model.  How much better is the random forest model?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "banking = pd.read_csv('data/banking.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp_var_rate</th>\n",
       "      <th>cons_price_idx</th>\n",
       "      <th>cons_conf_idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr_employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>basic.4y</th>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>basic.6y</th>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>basic.9y</th>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high.school</th>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>illiterate</th>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>professional.course</th>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>university.degree</th>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>1731</td>\n",
       "      <td>1731</td>\n",
       "      <td>1731</td>\n",
       "      <td>1731</td>\n",
       "      <td>1731</td>\n",
       "      <td>1731</td>\n",
       "      <td>1731</td>\n",
       "      <td>1731</td>\n",
       "      <td>1731</td>\n",
       "      <td>1731</td>\n",
       "      <td>1731</td>\n",
       "      <td>1731</td>\n",
       "      <td>1731</td>\n",
       "      <td>1731</td>\n",
       "      <td>1731</td>\n",
       "      <td>1731</td>\n",
       "      <td>1731</td>\n",
       "      <td>1731</td>\n",
       "      <td>1731</td>\n",
       "      <td>1731</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       age    job  marital  default  housing   loan  contact  \\\n",
       "education                                                                      \n",
       "basic.4y              4176   4176     4176     4176     4176   4176     4176   \n",
       "basic.6y              2292   2292     2292     2292     2292   2292     2292   \n",
       "basic.9y              6045   6045     6045     6045     6045   6045     6045   \n",
       "high.school           9515   9515     9515     9515     9515   9515     9515   \n",
       "illiterate              18     18       18       18       18     18       18   \n",
       "professional.course   5243   5243     5243     5243     5243   5243     5243   \n",
       "university.degree    12168  12168    12168    12168    12168  12168    12168   \n",
       "unknown               1731   1731     1731     1731     1731   1731     1731   \n",
       "\n",
       "                     month  day_of_week  duration  campaign  pdays  previous  \\\n",
       "education                                                                      \n",
       "basic.4y              4176         4176      4176      4176   4176      4176   \n",
       "basic.6y              2292         2292      2292      2292   2292      2292   \n",
       "basic.9y              6045         6045      6045      6045   6045      6045   \n",
       "high.school           9515         9515      9515      9515   9515      9515   \n",
       "illiterate              18           18        18        18     18        18   \n",
       "professional.course   5243         5243      5243      5243   5243      5243   \n",
       "university.degree    12168        12168     12168     12168  12168     12168   \n",
       "unknown               1731         1731      1731      1731   1731      1731   \n",
       "\n",
       "                     poutcome  emp_var_rate  cons_price_idx  cons_conf_idx  \\\n",
       "education                                                                    \n",
       "basic.4y                 4176          4176            4176           4176   \n",
       "basic.6y                 2292          2292            2292           2292   \n",
       "basic.9y                 6045          6045            6045           6045   \n",
       "high.school              9515          9515            9515           9515   \n",
       "illiterate                 18            18              18             18   \n",
       "professional.course      5243          5243            5243           5243   \n",
       "university.degree       12168         12168           12168          12168   \n",
       "unknown                  1731          1731            1731           1731   \n",
       "\n",
       "                     euribor3m  nr_employed      y  \n",
       "education                                           \n",
       "basic.4y                  4176         4176   4176  \n",
       "basic.6y                  2292         2292   2292  \n",
       "basic.9y                  6045         6045   6045  \n",
       "high.school               9515         9515   9515  \n",
       "illiterate                  18           18     18  \n",
       "professional.course       5243         5243   5243  \n",
       "university.degree        12168        12168  12168  \n",
       "unknown                   1731         1731   1731  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "banking.groupby('education').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>basic</th>\n",
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       "      <td>12513</td>\n",
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       "      <td>12513</td>\n",
       "      <td>12513</td>\n",
       "      <td>12513</td>\n",
       "      <td>12513</td>\n",
       "      <td>12513</td>\n",
       "      <td>12513</td>\n",
       "      <td>12513</td>\n",
       "      <td>12513</td>\n",
       "      <td>12513</td>\n",
       "      <td>12513</td>\n",
       "      <td>12513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high.school</th>\n",
       "      <td>9515</td>\n",
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       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
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       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>illiterate</th>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>professional.course</th>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
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       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
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       "    <tr>\n",
       "      <th>university.degree</th>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
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       "      <td>12168</td>\n",
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       "      <td>1731</td>\n",
       "      <td>1731</td>\n",
       "      <td>1731</td>\n",
       "      <td>1731</td>\n",
       "      <td>1731</td>\n",
       "      <td>1731</td>\n",
       "      <td>1731</td>\n",
       "      <td>1731</td>\n",
       "      <td>1731</td>\n",
       "      <td>1731</td>\n",
       "      <td>1731</td>\n",
       "      <td>1731</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       age    job  marital  default  housing   loan  contact  \\\n",
       "education                                                                      \n",
       "basic                12513  12513    12513    12513    12513  12513    12513   \n",
       "high.school           9515   9515     9515     9515     9515   9515     9515   \n",
       "illiterate              18     18       18       18       18     18       18   \n",
       "professional.course   5243   5243     5243     5243     5243   5243     5243   \n",
       "university.degree    12168  12168    12168    12168    12168  12168    12168   \n",
       "unknown               1731   1731     1731     1731     1731   1731     1731   \n",
       "\n",
       "                     month  day_of_week  duration  campaign  pdays  previous  \\\n",
       "education                                                                      \n",
       "basic                12513        12513     12513     12513  12513     12513   \n",
       "high.school           9515         9515      9515      9515   9515      9515   \n",
       "illiterate              18           18        18        18     18        18   \n",
       "professional.course   5243         5243      5243      5243   5243      5243   \n",
       "university.degree    12168        12168     12168     12168  12168     12168   \n",
       "unknown               1731         1731      1731      1731   1731      1731   \n",
       "\n",
       "                     poutcome  emp_var_rate  cons_price_idx  cons_conf_idx  \\\n",
       "education                                                                    \n",
       "basic                   12513         12513           12513          12513   \n",
       "high.school              9515          9515            9515           9515   \n",
       "illiterate                 18            18              18             18   \n",
       "professional.course      5243          5243            5243           5243   \n",
       "university.degree       12168         12168           12168          12168   \n",
       "unknown                  1731          1731            1731           1731   \n",
       "\n",
       "                     euribor3m  nr_employed      y  \n",
       "education                                           \n",
       "basic                    12513        12513  12513  \n",
       "high.school               9515         9515   9515  \n",
       "illiterate                  18           18     18  \n",
       "professional.course       5243         5243   5243  \n",
       "university.degree        12168        12168  12168  \n",
       "unknown                   1731         1731   1731  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wrong = {'basic.4y', 'basic.6y', 'basic.9y'}\n",
    "banking.education = ['basic' if x in wrong else x for x in banking.education]\n",
    "banking.groupby('education').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "banking.drop('duration', inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41188, 61)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_final = pd.get_dummies(banking)\n",
    "data_final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>emp_var_rate</th>\n",
       "      <th>cons_price_idx</th>\n",
       "      <th>cons_conf_idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr_employed</th>\n",
       "      <th>y</th>\n",
       "      <th>...</th>\n",
       "      <th>month_oct</th>\n",
       "      <th>month_sep</th>\n",
       "      <th>day_of_week_fri</th>\n",
       "      <th>day_of_week_mon</th>\n",
       "      <th>day_of_week_thu</th>\n",
       "      <th>day_of_week_tue</th>\n",
       "      <th>day_of_week_wed</th>\n",
       "      <th>poutcome_failure</th>\n",
       "      <th>poutcome_nonexistent</th>\n",
       "      <th>poutcome_success</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>93.444</td>\n",
       "      <td>-36.1</td>\n",
       "      <td>4.963</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>93.200</td>\n",
       "      <td>-42.0</td>\n",
       "      <td>4.021</td>\n",
       "      <td>5195.8</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>94.055</td>\n",
       "      <td>-39.8</td>\n",
       "      <td>0.729</td>\n",
       "      <td>4991.6</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>93.075</td>\n",
       "      <td>-47.1</td>\n",
       "      <td>1.405</td>\n",
       "      <td>5099.1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.9</td>\n",
       "      <td>92.201</td>\n",
       "      <td>-31.4</td>\n",
       "      <td>0.869</td>\n",
       "      <td>5076.2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  campaign  pdays  previous  emp_var_rate  cons_price_idx  \\\n",
       "0   44         1    999         0           1.4          93.444   \n",
       "1   53         1    999         0          -0.1          93.200   \n",
       "2   28         3      6         2          -1.7          94.055   \n",
       "3   39         2    999         0          -1.8          93.075   \n",
       "4   55         1      3         1          -2.9          92.201   \n",
       "\n",
       "   cons_conf_idx  euribor3m  nr_employed  y        ...         month_oct  \\\n",
       "0          -36.1      4.963       5228.1  0        ...                 0   \n",
       "1          -42.0      4.021       5195.8  0        ...                 0   \n",
       "2          -39.8      0.729       4991.6  1        ...                 0   \n",
       "3          -47.1      1.405       5099.1  0        ...                 0   \n",
       "4          -31.4      0.869       5076.2  1        ...                 0   \n",
       "\n",
       "   month_sep  day_of_week_fri  day_of_week_mon  day_of_week_thu  \\\n",
       "0          0                0                0                1   \n",
       "1          0                1                0                0   \n",
       "2          0                0                0                1   \n",
       "3          0                1                0                0   \n",
       "4          0                1                0                0   \n",
       "\n",
       "   day_of_week_tue  day_of_week_wed  poutcome_failure  poutcome_nonexistent  \\\n",
       "0                0                0                 0                     1   \n",
       "1                0                0                 0                     1   \n",
       "2                0                0                 0                     0   \n",
       "3                0                0                 0                     1   \n",
       "4                0                0                 0                     0   \n",
       "\n",
       "   poutcome_success  \n",
       "0                 0  \n",
       "1                 0  \n",
       "2                 1  \n",
       "3                 0  \n",
       "4                 1  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE**: We need to use .get_dummies() to one-hot encode the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add your code for data splitting (train_test_split)\n",
    "\n",
    "# df_iris['is_setosa'] = np.where(df_iris.species == 'setosa','setosa', 'not_setosa' )\n",
    "banking_train, banking_test = train_test_split(data_final, test_size = 0.3)\n",
    "# df_iris.sample(2)\n",
    "\n",
    "banking_train_dropy = banking_train.drop('y',axis=1)\n",
    "banking_test_dropy = banking_test.drop('y',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build a random forest\n",
    "# now do the predicting... use the same approach as you did in testing the Decision Tree Classifier above,\n",
    "# but don't include the .sample() line\n",
    "# this is testing the model \n",
    "\n",
    "rf_model = skens.RandomForestClassifier(n_estimators=10,oob_score=True, criterion='entropy')\n",
    "rf_model.fit(banking_train_dropy,banking_train.y)\n",
    "\n",
    "# now do the predicting... use the same approach as you did in testing the Decision Tree Classifier above,\n",
    "# but don't include the .sample() line\n",
    "# this is testing the model \n",
    "predicted_labels = rf_model.predict(banking_test_dropy)\n",
    "banking_test['predicted_rf_tree'] = predicted_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1dd6fac8>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check feature importance\n",
    "feat_importance = rf_model.feature_importances_\n",
    "pd.DataFrame({'Feature Importance':feat_importance},\n",
    "            index=banking_train_dropy.columns).plot(kind='barh', figsize=(30,20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, error_score='raise-deprecating',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
       "            max_depth=None, max_features='auto', 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, n_estimators=10, n_jobs=None,\n",
       "            oob_score=True, random_state=None, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'n_estimators': [5, 10, 15, 20, 25], 'max_depth': [2, 5, 7, 9]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Model Selection and Comparison\n",
    "\n",
    "param_grid = {\n",
    "                 'n_estimators': [5, 10, 15, 20, 25],\n",
    "                 'max_depth': [2, 5, 7, 9],\n",
    "             }\n",
    "\n",
    "grid_clf = GridSearchCV(rf_model, param_grid, cv=10)\n",
    "grid_clf.fit(banking_train_dropy,banking_train.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
       "            max_depth=9, max_features='auto', 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, n_estimators=20, n_jobs=None,\n",
       "            oob_score=True, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 9, 'n_estimators': 20}"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([0.05321372, 0.08937712, 0.12904708, 0.16317699, 0.19487982,\n",
       "        0.06785405, 0.12061017, 0.17462499, 0.21201911, 0.2708672 ,\n",
       "        0.07683685, 0.14135375, 0.19458408, 0.25820692, 0.32908127,\n",
       "        0.08824592, 0.16452065, 0.23099658, 0.29720447, 0.38914912]),\n",
       " 'std_fit_time': array([0.00887353, 0.00429635, 0.01701041, 0.00651222, 0.00758091,\n",
       "        0.00531152, 0.00448815, 0.00854117, 0.01329685, 0.01196303,\n",
       "        0.00505768, 0.00936485, 0.00848934, 0.01618429, 0.02398951,\n",
       "        0.00658314, 0.00635752, 0.01691771, 0.0160388 , 0.02392537]),\n",
       " 'mean_score_time': array([0.00259814, 0.00397453, 0.00500705, 0.00535614, 0.00581405,\n",
       "        0.0036485 , 0.00438888, 0.00509365, 0.00591195, 0.00802176,\n",
       "        0.00357113, 0.00462053, 0.00634112, 0.00777824, 0.00816524,\n",
       "        0.00347474, 0.00546424, 0.00707815, 0.0082114 , 0.00994496]),\n",
       " 'std_score_time': array([0.00031595, 0.000751  , 0.00210124, 0.00076813, 0.00044699,\n",
       "        0.00060976, 0.00105328, 0.00070659, 0.00073579, 0.00209239,\n",
       "        0.00025882, 0.00061812, 0.00095087, 0.00155167, 0.00056639,\n",
       "        0.0004804 , 0.00096423, 0.00167844, 0.00139283, 0.00156498]),\n",
       " 'param_max_depth': masked_array(data=[2, 2, 2, 2, 2, 5, 5, 5, 5, 5, 7, 7, 7, 7, 7, 9, 9, 9,\n",
       "                    9, 9],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_n_estimators': masked_array(data=[5, 10, 15, 20, 25, 5, 10, 15, 20, 25, 5, 10, 15, 20,\n",
       "                    25, 5, 10, 15, 20, 25],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'max_depth': 2, 'n_estimators': 5},\n",
       "  {'max_depth': 2, 'n_estimators': 10},\n",
       "  {'max_depth': 2, 'n_estimators': 15},\n",
       "  {'max_depth': 2, 'n_estimators': 20},\n",
       "  {'max_depth': 2, 'n_estimators': 25},\n",
       "  {'max_depth': 5, 'n_estimators': 5},\n",
       "  {'max_depth': 5, 'n_estimators': 10},\n",
       "  {'max_depth': 5, 'n_estimators': 15},\n",
       "  {'max_depth': 5, 'n_estimators': 20},\n",
       "  {'max_depth': 5, 'n_estimators': 25},\n",
       "  {'max_depth': 7, 'n_estimators': 5},\n",
       "  {'max_depth': 7, 'n_estimators': 10},\n",
       "  {'max_depth': 7, 'n_estimators': 15},\n",
       "  {'max_depth': 7, 'n_estimators': 20},\n",
       "  {'max_depth': 7, 'n_estimators': 25},\n",
       "  {'max_depth': 9, 'n_estimators': 5},\n",
       "  {'max_depth': 9, 'n_estimators': 10},\n",
       "  {'max_depth': 9, 'n_estimators': 15},\n",
       "  {'max_depth': 9, 'n_estimators': 20},\n",
       "  {'max_depth': 9, 'n_estimators': 25}],\n",
       " 'split0_test_score': array([0.88765603, 0.88973648, 0.88730929, 0.88765603, 0.88661581,\n",
       "        0.89944521, 0.89736477, 0.89805825, 0.89944521, 0.89840499,\n",
       "        0.89771151, 0.9001387 , 0.90325936, 0.89875173, 0.89979196,\n",
       "        0.9001387 , 0.90221914, 0.90083218, 0.90533981, 0.90395284]),\n",
       " 'split1_test_score': array([0.88661581, 0.88800277, 0.88765603, 0.88661581, 0.88661581,\n",
       "        0.8925104 , 0.89701803, 0.89701803, 0.89597781, 0.89667129,\n",
       "        0.89563107, 0.89459085, 0.89840499, 0.89805825, 0.89701803,\n",
       "        0.89597781, 0.89667129, 0.89771151, 0.89875173, 0.89771151]),\n",
       " 'split2_test_score': array([0.88692334, 0.88796393, 0.88692334, 0.88692334, 0.8872702 ,\n",
       "        0.90218522, 0.90183836, 0.90461325, 0.90530697, 0.90496011,\n",
       "        0.90496011, 0.90357267, 0.90496011, 0.90391953, 0.90426639,\n",
       "        0.90565383, 0.90496011, 0.90530697, 0.9094693 , 0.90600069]),\n",
       " 'split3_test_score': array([0.88831079, 0.88692334, 0.88692334, 0.88900451, 0.8872702 ,\n",
       "        0.90357267, 0.90183836, 0.90322581, 0.90079778, 0.90079778,\n",
       "        0.90322581, 0.90357267, 0.90322581, 0.90391953, 0.90391953,\n",
       "        0.90253208, 0.90183836, 0.90183836, 0.90357267, 0.90322581]),\n",
       " 'split4_test_score': array([0.89004509, 0.89316684, 0.89039195, 0.88692334, 0.88761707,\n",
       "        0.9014915 , 0.89941034, 0.90287895, 0.90079778, 0.9014915 ,\n",
       "        0.89802289, 0.90391953, 0.90010406, 0.90287895, 0.90218522,\n",
       "        0.89802289, 0.90079778, 0.90183836, 0.90253208, 0.90045092]),\n",
       " 'split5_test_score': array([0.8872702 , 0.88692334, 0.88692334, 0.88657648, 0.88692334,\n",
       "        0.89836975, 0.89767603, 0.89663545, 0.89594173, 0.89732917,\n",
       "        0.89455428, 0.89871661, 0.89941034, 0.89594173, 0.89836975,\n",
       "        0.89698231, 0.90183836, 0.89663545, 0.89871661, 0.89802289]),\n",
       " 'split6_test_score': array([0.88692334, 0.8917794 , 0.89004509, 0.88692334, 0.88865765,\n",
       "        0.89628859, 0.89524801, 0.89628859, 0.89594173, 0.89628859,\n",
       "        0.89386056, 0.89732917, 0.89420742, 0.89594173, 0.89628859,\n",
       "        0.89767603, 0.89559487, 0.89732917, 0.89663545, 0.89836975]),\n",
       " 'split7_test_score': array([0.88935137, 0.88692334, 0.88692334, 0.88692334, 0.89247312,\n",
       "        0.89247312, 0.89316684, 0.89386056, 0.89490114, 0.89386056,\n",
       "        0.88935137, 0.89316684, 0.8935137 , 0.89247312, 0.89420742,\n",
       "        0.89490114, 0.89420742, 0.88831079, 0.89420742, 0.8917794 ]),\n",
       " 'split8_test_score': array([0.89386056, 0.88692334, 0.88692334, 0.88969823, 0.88692334,\n",
       "        0.89871661, 0.89767603, 0.89767603, 0.89836975, 0.89802289,\n",
       "        0.89663545, 0.89802289, 0.8997572 , 0.89732917, 0.89802289,\n",
       "        0.89559487, 0.8997572 , 0.89628859, 0.89663545, 0.8997572 ]),\n",
       " 'split9_test_score': array([0.89902845, 0.88827203, 0.88688411, 0.8907009 , 0.88757807,\n",
       "        0.89729355, 0.89972241, 0.89868147, 0.89798751, 0.89902845,\n",
       "        0.8962526 , 0.89659958, 0.89937543, 0.89902845, 0.89798751,\n",
       "        0.89764053, 0.89590562, 0.8945177 , 0.89868147, 0.9000694 ]),\n",
       " 'mean_test_score': array([0.889598  , 0.88866151, 0.88769033, 0.88779439, 0.88779439,\n",
       "        0.89823454, 0.8980958 , 0.89889355, 0.8985467 , 0.89868544,\n",
       "        0.89702057, 0.89896292, 0.89962193, 0.89882418, 0.89920572,\n",
       "        0.89851202, 0.89937914, 0.89806111, 0.90045437, 0.8999341 ]),\n",
       " 'std_test_score': array([0.00376005, 0.00210549, 0.00128766, 0.00139499, 0.00166009,\n",
       "        0.00356464, 0.0025853 , 0.00332149, 0.00300745, 0.00295401,\n",
       "        0.00424937, 0.00361053, 0.00350971, 0.00358028, 0.00314499,\n",
       "        0.00319092, 0.0033753 , 0.00449627, 0.00443208, 0.00376361]),\n",
       " 'rank_test_score': array([16, 17, 20, 18, 18, 12, 13,  7, 10,  9, 15,  6,  3,  8,  5, 11,  4,\n",
       "        14,  1,  2], dtype=int32),\n",
       " 'split0_train_score': array([0.8870775 , 0.89000655, 0.88792539, 0.88765561, 0.88784831,\n",
       "        0.89914056, 0.89925618, 0.89933326, 0.90029676, 0.8995645 ,\n",
       "        0.90418931, 0.90237792, 0.90253208, 0.90337997, 0.9020696 ,\n",
       "        0.90950784, 0.91012448, 0.90950784, 0.91058697, 0.90835164]),\n",
       " 'split1_train_score': array([0.8868848 , 0.88842641, 0.88800247, 0.8868848 , 0.8872702 ,\n",
       "        0.89752187, 0.89933326, 0.89952596, 0.89987282, 0.90025822,\n",
       "        0.90203106, 0.90214668, 0.90102902, 0.90230084, 0.9020696 ,\n",
       "        0.91074113, 0.91166609, 0.9090839 , 0.91020156, 0.90919952]),\n",
       " 'split2_train_score': array([0.88685062, 0.88789117, 0.88685062, 0.88696624, 0.88688916,\n",
       "        0.89887467, 0.8997996 , 0.89941421, 0.89887467, 0.89899029,\n",
       "        0.90172653, 0.90195776, 0.90180361, 0.90118699, 0.90253584,\n",
       "        0.90700632, 0.90800832, 0.90800832, 0.90962695, 0.90685217]),\n",
       " 'split3_train_score': array([0.88781409, 0.88750578, 0.88685062, 0.88854632, 0.88704332,\n",
       "        0.89987668, 0.89906737, 0.89949129, 0.89976106, 0.8992986 ,\n",
       "        0.90403885, 0.90388469, 0.90195776, 0.90203484, 0.90215045,\n",
       "        0.90839371, 0.90989672, 0.91116849, 0.91132265, 0.91035918]),\n",
       " 'split4_train_score': array([0.88792971, 0.8949052 , 0.88846925, 0.88685062, 0.88708186,\n",
       "        0.90003083, 0.89922152, 0.8992986 , 0.89922152, 0.89933714,\n",
       "        0.9019963 , 0.90257438, 0.90326807, 0.90334515, 0.90130261,\n",
       "        0.9097811 , 0.90974256, 0.90893325, 0.90920302, 0.91039772]),\n",
       " 'split5_train_score': array([0.88889317, 0.88685062, 0.88685062, 0.8869277 , 0.88685062,\n",
       "        0.8997996 , 0.90003083, 0.8997996 , 0.89987668, 0.89964544,\n",
       "        0.90188068, 0.90322954, 0.90307538, 0.90357638, 0.904193  ,\n",
       "        0.90908741, 0.91093726, 0.90966548, 0.91070603, 0.91105287]),\n",
       " 'split6_train_score': array([0.88685062, 0.8913211 , 0.88970248, 0.88685062, 0.89078156,\n",
       "        0.89976106, 0.90064745, 0.89999229, 0.90053183, 0.90053183,\n",
       "        0.90253584, 0.90438569, 0.90242023, 0.90365346, 0.90245876,\n",
       "        0.9070834 , 0.90820102, 0.91008941, 0.91136118, 0.90920302]),\n",
       " 'split7_train_score': array([0.89012641, 0.88685062, 0.88685062, 0.88685062, 0.8920148 ,\n",
       "        0.90053183, 0.90060891, 0.90068599, 0.90033914, 0.90060891,\n",
       "        0.90473254, 0.90480962, 0.90295977, 0.90288269, 0.9024973 ,\n",
       "        0.90835517, 0.91105287, 0.91128411, 0.91032064, 0.91166949]),\n",
       " 'split8_train_score': array([0.89440419, 0.88685062, 0.88685062, 0.89024202, 0.88685062,\n",
       "        0.89991522, 0.89991522, 0.90010791, 0.89891321, 0.89945275,\n",
       "        0.90415446, 0.90392323, 0.90400031, 0.90253584, 0.90276707,\n",
       "        0.90866348, 0.90820102, 0.90850933, 0.91032064, 0.91012795]),\n",
       " 'split9_train_score': array([0.8992254 , 0.8880111 , 0.88685498, 0.89120968, 0.88882038,\n",
       "        0.89918687, 0.89968785, 0.89945663, 0.89957224, 0.89995761,\n",
       "        0.90415816, 0.9035801 , 0.90311765, 0.90307912, 0.90226984,\n",
       "        0.91132606, 0.91144167, 0.90986165, 0.9097075 , 0.91074801]),\n",
       " 'mean_train_score': array([0.88960565, 0.88886192, 0.88752077, 0.88789843, 0.88814508,\n",
       "        0.89946392, 0.89975682, 0.89971057, 0.89972599, 0.89976453,\n",
       "        0.90314437, 0.90328696, 0.90261639, 0.90279753, 0.90243141,\n",
       "        0.90899456, 0.9099272 , 0.90961118, 0.91033571, 0.90979616]),\n",
       " 'std_train_score': array([0.00389022, 0.00244356, 0.00093509, 0.00151881, 0.00174666,\n",
       "        0.00079605, 0.00053085, 0.00041751, 0.00055318, 0.00052281,\n",
       "        0.00114006, 0.00093692, 0.00081114, 0.00074536, 0.00069806,\n",
       "        0.0013365 , 0.00131606, 0.00100249, 0.00066516, 0.00135075])}"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_clf.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.889598  , 0.88866151, 0.88769033, 0.88779439, 0.88779439,\n",
       "       0.89823454, 0.8980958 , 0.89889355, 0.8985467 , 0.89868544,\n",
       "       0.89702057, 0.89896292, 0.89962193, 0.89882418, 0.89920572,\n",
       "       0.89851202, 0.89937914, 0.89806111, 0.90045437, 0.8999341 ])"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_clf.cv_results_['mean_test_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9004543720301065"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_clf.cv_results_['mean_test_score'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best random forest model has the 'max_depth': 9, 'n_estimators': 20, which is also the same position for specific parameter 9 and 20. And the accuracy is around 90%. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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