Class-Decision-Trees-drug(1).ipynb

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    {
      "cell_type": "markdown",
      "source": "In this lab exercise, you will learn a popular machine learning algorithm, Decision Trees. You will use this classification algorithm to build a model from the historical data of patients, and their response to different medications. Then you will use the trained decision tree to predict the class of an unknown patient, or to find a proper drug for a new patient.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<h1>Table of contents</h1>\n\n<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n    <ol>\n        <li><a href=\"#about_dataset\">About the dataset</a></li>\n        <li><a href=\"#downloading_data\">Downloading the Data</a></li>\n        <li><a href=\"#pre-processing\">Pre-processing</a></li>\n        <li><a href=\"#setting_up_tree\">Setting up the Decision Tree</a></li>\n        <li><a href=\"#modeling\">Modeling</a></li>\n        <li><a href=\"#prediction\">Prediction</a></li>\n        <li><a href=\"#evaluation\">Evaluation</a></li>\n        <li><a href=\"#visualization\">Visualization</a></li>\n    </ol>\n</div>\n<br>\n<hr>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Import the Following Libraries:\n<ul>\n    <li> <b>numpy (as np)</b> </li>\n    <li> <b>pandas</b> </li>\n    <li> <b>DecisionTreeClassifier</b> from <b>sklearn.tree</b> </li>\n</ul>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "if you uisng you own version comment out\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import piplite\nawait piplite.install(['pandas'])\nawait piplite.install(['matplotlib'])\nawait piplite.install(['numpy'])\nawait piplite.install(['scikit-learn'])",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 1
    },
    {
      "cell_type": "code",
      "source": "import numpy as np \nimport pandas as pd\nfrom sklearn.tree import DecisionTreeClassifier\nimport sklearn.tree as tree",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stderr",
          "text": "<ipython-input-2-022ebe2b48ba>:2: DeprecationWarning: \nPyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\nbut was not found to be installed on your system.\nIf this would cause problems for you,\nplease provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n        \n  import pandas as pd\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 2
    },
    {
      "cell_type": "code",
      "source": "from pyodide.http import pyfetch\n\nasync def download(url, filename):\n    response = await pyfetch(url)\n    if response.status == 200:\n        with open(filename, \"wb\") as f:\n            f.write(await response.bytes())",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 4
    },
    {
      "cell_type": "markdown",
      "source": "<div id=\"about_dataset\">\n    <h2>About the dataset</h2>\n    Imagine that you are a medical researcher compiling data for a study. You have collected data about a set of patients, all of whom suffered from the same illness. During their course of treatment, each patient responded to one of 5 medications, Drug A, Drug B, Drug c, Drug x and y. \n    <br>\n    <br>\n    Part of your job is to build a model to find out which drug might be appropriate for a future patient with the same illness. The features of this dataset are Age, Sex, Blood Pressure, and the Cholesterol of the patients, and the target is the drug that each patient responded to.\n    <br>\n    <br>\n    It is a sample of multiclass classifier, and you can use the training part of the dataset \n    to build a decision tree, and then use it to predict the class of an unknown patient, or to prescribe a drug to a new patient.\n</div>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<div id=\"downloading_data\"> \n    <h2>Downloading the Data</h2>\n    To download the data, we will use !wget to download it from IBM Object Storage.\n</div>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "path= 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/drug200.csv'\nawait download(path,\"drug200.csv\")\npath=\"drug200.csv\"",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 5
    },
    {
      "cell_type": "markdown",
      "source": "Now, read the data using pandas dataframe:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "my_data = pd.read_csv(\"drug200.csv\", delimiter=\",\")\nmy_data[0:5]",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 6,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   Age Sex      BP Cholesterol  Na_to_K   Drug\n0   23   F    HIGH        HIGH   25.355  drugY\n1   47   M     LOW        HIGH   13.093  drugC\n2   47   M     LOW        HIGH   10.114  drugC\n3   28   F  NORMAL        HIGH    7.798  drugX\n4   61   F     LOW        HIGH   18.043  drugY",
            "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>Sex</th>\n      <th>BP</th>\n      <th>Cholesterol</th>\n      <th>Na_to_K</th>\n      <th>Drug</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>23</td>\n      <td>F</td>\n      <td>HIGH</td>\n      <td>HIGH</td>\n      <td>25.355</td>\n      <td>drugY</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>47</td>\n      <td>M</td>\n      <td>LOW</td>\n      <td>HIGH</td>\n      <td>13.093</td>\n      <td>drugC</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>47</td>\n      <td>M</td>\n      <td>LOW</td>\n      <td>HIGH</td>\n      <td>10.114</td>\n      <td>drugC</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>28</td>\n      <td>F</td>\n      <td>NORMAL</td>\n      <td>HIGH</td>\n      <td>7.798</td>\n      <td>drugX</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>61</td>\n      <td>F</td>\n      <td>LOW</td>\n      <td>HIGH</td>\n      <td>18.043</td>\n      <td>drugY</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 6
    },
    {
      "cell_type": "markdown",
      "source": "<div id=\"practice\"> \n    <h3>Practice</h3> \n    What is the size of data? \n</div>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# write your code here\nmy_data.shape",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 7,
          "output_type": "execute_result",
          "data": {
            "text/plain": "(200, 6)"
          },
          "metadata": {}
        }
      ],
      "execution_count": 7
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for the solution</summary>\n\n```python\nmy_data.shape\n\n```\n\n</details>\n\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<div href=\"pre-processing\">\n    <h2>Pre-processing</h2>\n</div>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Using <b>my_data</b> as the Drug.csv data read by pandas, declare the following variables: <br>\n\n<ul>\n    <li> <b> X </b> as the <b> Feature Matrix </b> (data of my_data) </li>\n    <li> <b> y </b> as the <b> response vector </b> (target) </li>\n</ul>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Remove the column containing the target name since it doesn't contain numeric values.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "X = my_data[['Age', 'Sex', 'BP', 'Cholesterol', 'Na_to_K']].values\nX[0:5]",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 8,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([[23, 'F', 'HIGH', 'HIGH', 25.355],\n       [47, 'M', 'LOW', 'HIGH', 13.093],\n       [47, 'M', 'LOW', 'HIGH', 10.114],\n       [28, 'F', 'NORMAL', 'HIGH', 7.798],\n       [61, 'F', 'LOW', 'HIGH', 18.043]], dtype=object)"
          },
          "metadata": {}
        }
      ],
      "execution_count": 8
    },
    {
      "cell_type": "markdown",
      "source": "As you may figure out, some features in this dataset are categorical, such as __Sex__ or __BP__. Unfortunately, Sklearn Decision Trees does not handle categorical variables. We can still convert these features to numerical values using __LabelEncoder__\nto convert the categorical variable into numerical variables.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn import preprocessing\nle_sex = preprocessing.LabelEncoder()\nle_sex.fit(['F','M'])\nX[:,1] = le_sex.transform(X[:,1]) \n\n\nle_BP = preprocessing.LabelEncoder()\nle_BP.fit(['LOW', 'NORMAL', 'HIGH'])\nX[:,2] = le_BP.transform(X[:,2])\n\n\nle_Chol = preprocessing.LabelEncoder()\nle_Chol.fit([ 'NORMAL', 'HIGH'])\nX[:,3] = le_Chol.transform(X[:,3]) \n\nX[0:5]",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 9,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([[23, 0, 0, 0, 25.355],\n       [47, 1, 1, 0, 13.093],\n       [47, 1, 1, 0, 10.114],\n       [28, 0, 2, 0, 7.798],\n       [61, 0, 1, 0, 18.043]], dtype=object)"
          },
          "metadata": {}
        }
      ],
      "execution_count": 9
    },
    {
      "cell_type": "markdown",
      "source": "Now we can fill the target variable.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "y = my_data[\"Drug\"]\ny[0:5]",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 10,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0    drugY\n1    drugC\n2    drugC\n3    drugX\n4    drugY\nName: Drug, dtype: object"
          },
          "metadata": {}
        }
      ],
      "execution_count": 10
    },
    {
      "cell_type": "markdown",
      "source": "<hr>\n\n<div id=\"setting_up_tree\">\n    <h2>Setting up the Decision Tree</h2>\n    We will be using <b>train/test split</b> on our <b>decision tree</b>. Let's import <b>train_test_split</b> from <b>sklearn.cross_validation</b>.\n</div>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn.model_selection import train_test_split",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 11
    },
    {
      "cell_type": "markdown",
      "source": "Now <b> train_test_split </b> will return 4 different parameters. We will name them:<br>\nX_trainset, X_testset, y_trainset, y_testset <br> <br>\nThe <b> train_test_split </b> will need the parameters: <br>\nX, y, test_size=0.3, and random_state=3. <br> <br>\nThe <b>X</b> and <b>y</b> are the arrays required before the split, the <b>test_size</b> represents the ratio of the testing dataset, and the <b>random_state</b> ensures that we obtain the same splits.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "X_trainset, X_testset, y_trainset, y_testset = train_test_split(X, y, test_size=0.3, random_state=3)",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 13
    },
    {
      "cell_type": "markdown",
      "source": "<h3>Practice</h3>\nPrint the shape of X_trainset and y_trainset. Ensure that the dimensions match.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# your code\nprint('Shape of X training set {}'.format(X_trainset.shape),'&',' Size of Y training set {}'.format(y_trainset.shape))",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "Shape of X training set (140, 5) &  Size of Y training set (140,)\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 14
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for the solution</summary>\n\n```python\nprint('Shape of X training set {}'.format(X_trainset.shape),'&',' Size of Y training set {}'.format(y_trainset.shape))\n\n```\n\n</details>\n\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Print the shape of X_testset and y_testset. Ensure that the dimensions match.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# your code\nprint('Shape of X test set {}'.format(X_testset.shape),'&','Size of Y test set {}'.format(y_trainset.shape))\n",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "Shape of X test set (60, 5) & Size of Y test set (140,)\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 15
    },
    {
      "cell_type": "markdown",
      "source": "<details><summary>Click here for the solution</summary>\n\n```python\nprint('Shape of X test set {}'.format(X_testset.shape),'&','Size of y test set {}'.format(y_testset.shape))\n\n```\n\n</details>\n\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<hr>\n\n<div id=\"modeling\">\n    <h2>Modeling</h2>\n    We will first create an instance of the <b>DecisionTreeClassifier</b> called <b>drugTree</b>.<br>\n    Inside of the classifier, specify <i> criterion=\"entropy\" </i> so we can see the information gain of each node.\n</div>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "drugTree = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 4)\ndrugTree # it shows the default parameters",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 16,
          "output_type": "execute_result",
          "data": {
            "text/plain": "DecisionTreeClassifier(criterion='entropy', max_depth=4)",
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It is overwritten whether we have a\n  specific estimator or a Pipeline/ColumnTransformer */\n  background-color: var(--sklearn-color-background);\n}\n\n/* Toggleable label */\n#sk-container-id-1 label.sk-toggleable__label {\n  cursor: pointer;\n  display: block;\n  width: 100%;\n  margin-bottom: 0;\n  padding: 0.5em;\n  box-sizing: border-box;\n  text-align: center;\n}\n\n#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n  /* Arrow on the left of the label */\n  content: \"▸\";\n  float: left;\n  margin-right: 0.25em;\n  color: var(--sklearn-color-icon);\n}\n\n#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n  color: var(--sklearn-color-text);\n}\n\n/* Toggleable content - dropdown */\n\n#sk-container-id-1 div.sk-toggleable__content {\n  max-height: 0;\n  max-width: 0;\n  overflow: hidden;\n  text-align: left;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-1 div.sk-toggleable__content.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-1 div.sk-toggleable__content pre {\n  margin: 0.2em;\n  border-radius: 0.25em;\n  color: var(--sklearn-color-text);\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n  /* unfitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n  /* Expand drop-down */\n  max-height: 200px;\n  max-width: 100%;\n  overflow: auto;\n}\n\n#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n  content: \"▾\";\n}\n\n/* Pipeline/ColumnTransformer-specific style */\n\n#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator-specific style */\n\n/* Colorize estimator box */\n#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n#sk-container-id-1 div.sk-label label {\n  /* The background is the default theme color */\n  color: var(--sklearn-color-text-on-default-background);\n}\n\n/* On hover, darken the color of the background */\n#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n/* Label box, darken color on hover, fitted */\n#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator label */\n\n#sk-container-id-1 div.sk-label label {\n  font-family: monospace;\n  font-weight: bold;\n  display: inline-block;\n  line-height: 1.2em;\n}\n\n#sk-container-id-1 div.sk-label-container {\n  text-align: center;\n}\n\n/* Estimator-specific */\n#sk-container-id-1 div.sk-estimator {\n  font-family: monospace;\n  border: 1px dotted var(--sklearn-color-border-box);\n  border-radius: 0.25em;\n  box-sizing: border-box;\n  margin-bottom: 0.5em;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-1 div.sk-estimator.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n/* on hover */\n#sk-container-id-1 div.sk-estimator:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-1 div.sk-estimator.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Specification for estimator info (e.g. \"i\" and \"?\") */\n\n/* Common style for \"i\" and \"?\" */\n\n.sk-estimator-doc-link,\na:link.sk-estimator-doc-link,\na:visited.sk-estimator-doc-link {\n  float: right;\n  font-size: smaller;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1em;\n  height: 1em;\n  width: 1em;\n  text-decoration: none !important;\n  margin-left: 1ex;\n  /* unfitted */\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n  color: var(--sklearn-color-unfitted-level-1);\n}\n\n.sk-estimator-doc-link.fitted,\na:link.sk-estimator-doc-link.fitted,\na:visited.sk-estimator-doc-link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\ndiv.sk-estimator:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\ndiv.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n/* Span, style for the box shown on hovering the info icon */\n.sk-estimator-doc-link span {\n  display: none;\n  z-index: 9999;\n  position: relative;\n  font-weight: normal;\n  right: .2ex;\n  padding: .5ex;\n  margin: .5ex;\n  width: min-content;\n  min-width: 20ex;\n  max-width: 50ex;\n  color: var(--sklearn-color-text);\n  box-shadow: 2pt 2pt 4pt #999;\n  /* unfitted */\n  background: var(--sklearn-color-unfitted-level-0);\n  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n}\n\n.sk-estimator-doc-link.fitted span {\n  /* fitted */\n  background: var(--sklearn-color-fitted-level-0);\n  border: var(--sklearn-color-fitted-level-3);\n}\n\n.sk-estimator-doc-link:hover span {\n  display: block;\n}\n\n/* \"?\"-specific style due to the `<a>` HTML tag */\n\n#sk-container-id-1 a.estimator_doc_link {\n  float: right;\n  font-size: 1rem;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1rem;\n  height: 1rem;\n  width: 1rem;\n  text-decoration: none;\n  /* unfitted */\n  color: var(--sklearn-color-unfitted-level-1);\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n}\n\n#sk-container-id-1 a.estimator_doc_link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\n#sk-container-id-1 a.estimator_doc_link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n}\n</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=4)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=4)</pre></div> </div></div></div></div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 16
    },
    {
      "cell_type": "markdown",
      "source": "Next, we will fit the data with the training feature matrix <b> X_trainset </b> and training  response vector <b> y_trainset </b>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "drugTree.fit(X_trainset,y_trainset)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 17,
          "output_type": "execute_result",
          "data": {
            "text/plain": "DecisionTreeClassifier(criterion='entropy', max_depth=4)",
            "text/html": "<style>#sk-container-id-2 {\n  /* Definition of color scheme common for light and dark mode */\n  --sklearn-color-text: black;\n  --sklearn-color-line: gray;\n  /* Definition of color scheme for unfitted estimators */\n  --sklearn-color-unfitted-level-0: #fff5e6;\n  --sklearn-color-unfitted-level-1: #f6e4d2;\n  --sklearn-color-unfitted-level-2: #ffe0b3;\n  --sklearn-color-unfitted-level-3: chocolate;\n  /* Definition of color scheme for fitted estimators */\n  --sklearn-color-fitted-level-0: #f0f8ff;\n  --sklearn-color-fitted-level-1: #d4ebff;\n  --sklearn-color-fitted-level-2: #b3dbfd;\n  --sklearn-color-fitted-level-3: cornflowerblue;\n\n  /* Specific color for light theme */\n  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-icon: #696969;\n\n  @media (prefers-color-scheme: dark) {\n    /* Redefinition of color scheme for dark theme */\n    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-icon: #878787;\n  }\n}\n\n#sk-container-id-2 {\n  color: var(--sklearn-color-text);\n}\n\n#sk-container-id-2 pre {\n  padding: 0;\n}\n\n#sk-container-id-2 input.sk-hidden--visually {\n  border: 0;\n  clip: rect(1px 1px 1px 1px);\n  clip: rect(1px, 1px, 1px, 1px);\n  height: 1px;\n  margin: -1px;\n  overflow: hidden;\n  padding: 0;\n  position: absolute;\n  width: 1px;\n}\n\n#sk-container-id-2 div.sk-dashed-wrapped {\n  border: 1px dashed var(--sklearn-color-line);\n  margin: 0 0.4em 0.5em 0.4em;\n  box-sizing: border-box;\n  padding-bottom: 0.4em;\n  background-color: var(--sklearn-color-background);\n}\n\n#sk-container-id-2 div.sk-container {\n  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n     but bootstrap.min.css set `[hidden] { display: none !important; }`\n     so we also need the `!important` here to be able to override the\n     default hidden behavior on the sphinx rendered scikit-learn.org.\n     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n  display: inline-block !important;\n  position: relative;\n}\n\n#sk-container-id-2 div.sk-text-repr-fallback {\n  display: none;\n}\n\ndiv.sk-parallel-item,\ndiv.sk-serial,\ndiv.sk-item {\n  /* draw centered vertical line to link estimators */\n  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n  background-size: 2px 100%;\n  background-repeat: no-repeat;\n  background-position: center center;\n}\n\n/* Parallel-specific style estimator block */\n\n#sk-container-id-2 div.sk-parallel-item::after {\n  content: \"\";\n  width: 100%;\n  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n  flex-grow: 1;\n}\n\n#sk-container-id-2 div.sk-parallel {\n  display: flex;\n  align-items: stretch;\n  justify-content: center;\n  background-color: var(--sklearn-color-background);\n  position: relative;\n}\n\n#sk-container-id-2 div.sk-parallel-item {\n  display: flex;\n  flex-direction: column;\n}\n\n#sk-container-id-2 div.sk-parallel-item:first-child::after {\n  align-self: flex-end;\n  width: 50%;\n}\n\n#sk-container-id-2 div.sk-parallel-item:last-child::after {\n  align-self: flex-start;\n  width: 50%;\n}\n\n#sk-container-id-2 div.sk-parallel-item:only-child::after {\n  width: 0;\n}\n\n/* Serial-specific style estimator block */\n\n#sk-container-id-2 div.sk-serial {\n  display: flex;\n  flex-direction: column;\n  align-items: center;\n  background-color: var(--sklearn-color-background);\n  padding-right: 1em;\n  padding-left: 1em;\n}\n\n\n/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\nclickable and can be expanded/collapsed.\n- Pipeline and ColumnTransformer use this feature and define the default style\n- Estimators will overwrite some part of the style using the `sk-estimator` class\n*/\n\n/* Pipeline and ColumnTransformer style (default) */\n\n#sk-container-id-2 div.sk-toggleable {\n  /* Default theme specific background. It is overwritten whether we have a\n  specific estimator or a Pipeline/ColumnTransformer */\n  background-color: var(--sklearn-color-background);\n}\n\n/* Toggleable label */\n#sk-container-id-2 label.sk-toggleable__label {\n  cursor: pointer;\n  display: block;\n  width: 100%;\n  margin-bottom: 0;\n  padding: 0.5em;\n  box-sizing: border-box;\n  text-align: center;\n}\n\n#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n  /* Arrow on the left of the label */\n  content: \"▸\";\n  float: left;\n  margin-right: 0.25em;\n  color: var(--sklearn-color-icon);\n}\n\n#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n  color: var(--sklearn-color-text);\n}\n\n/* Toggleable content - dropdown */\n\n#sk-container-id-2 div.sk-toggleable__content {\n  max-height: 0;\n  max-width: 0;\n  overflow: hidden;\n  text-align: left;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-2 div.sk-toggleable__content.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-2 div.sk-toggleable__content pre {\n  margin: 0.2em;\n  border-radius: 0.25em;\n  color: var(--sklearn-color-text);\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n  /* unfitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n  /* Expand drop-down */\n  max-height: 200px;\n  max-width: 100%;\n  overflow: auto;\n}\n\n#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n  content: \"▾\";\n}\n\n/* Pipeline/ColumnTransformer-specific style */\n\n#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator-specific style */\n\n/* Colorize estimator box */\n#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n#sk-container-id-2 div.sk-label label {\n  /* The background is the default theme color */\n  color: var(--sklearn-color-text-on-default-background);\n}\n\n/* On hover, darken the color of the background */\n#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n/* Label box, darken color on hover, fitted */\n#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator label */\n\n#sk-container-id-2 div.sk-label label {\n  font-family: monospace;\n  font-weight: bold;\n  display: inline-block;\n  line-height: 1.2em;\n}\n\n#sk-container-id-2 div.sk-label-container {\n  text-align: center;\n}\n\n/* Estimator-specific */\n#sk-container-id-2 div.sk-estimator {\n  font-family: monospace;\n  border: 1px dotted var(--sklearn-color-border-box);\n  border-radius: 0.25em;\n  box-sizing: border-box;\n  margin-bottom: 0.5em;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-2 div.sk-estimator.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n/* on hover */\n#sk-container-id-2 div.sk-estimator:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-2 div.sk-estimator.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Specification for estimator info (e.g. \"i\" and \"?\") */\n\n/* Common style for \"i\" and \"?\" */\n\n.sk-estimator-doc-link,\na:link.sk-estimator-doc-link,\na:visited.sk-estimator-doc-link {\n  float: right;\n  font-size: smaller;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1em;\n  height: 1em;\n  width: 1em;\n  text-decoration: none !important;\n  margin-left: 1ex;\n  /* unfitted */\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n  color: var(--sklearn-color-unfitted-level-1);\n}\n\n.sk-estimator-doc-link.fitted,\na:link.sk-estimator-doc-link.fitted,\na:visited.sk-estimator-doc-link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\ndiv.sk-estimator:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\ndiv.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n/* Span, style for the box shown on hovering the info icon */\n.sk-estimator-doc-link span {\n  display: none;\n  z-index: 9999;\n  position: relative;\n  font-weight: normal;\n  right: .2ex;\n  padding: .5ex;\n  margin: .5ex;\n  width: min-content;\n  min-width: 20ex;\n  max-width: 50ex;\n  color: var(--sklearn-color-text);\n  box-shadow: 2pt 2pt 4pt #999;\n  /* unfitted */\n  background: var(--sklearn-color-unfitted-level-0);\n  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n}\n\n.sk-estimator-doc-link.fitted span {\n  /* fitted */\n  background: var(--sklearn-color-fitted-level-0);\n  border: var(--sklearn-color-fitted-level-3);\n}\n\n.sk-estimator-doc-link:hover span {\n  display: block;\n}\n\n/* \"?\"-specific style due to the `<a>` HTML tag */\n\n#sk-container-id-2 a.estimator_doc_link {\n  float: right;\n  font-size: 1rem;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1rem;\n  height: 1rem;\n  width: 1rem;\n  text-decoration: none;\n  /* unfitted */\n  color: var(--sklearn-color-unfitted-level-1);\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n}\n\n#sk-container-id-2 a.estimator_doc_link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\n#sk-container-id-2 a.estimator_doc_link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n}\n</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=4)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=4)</pre></div> </div></div></div></div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 17
    },
    {
      "cell_type": "markdown",
      "source": "<hr>\n\n<div id=\"prediction\">\n    <h2>Prediction</h2>\n    Let's make some <b>predictions</b> on the testing dataset and store it into a variable called <b>predTree</b>.\n</div>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "predTree = drugTree.predict(X_testset)",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 18
    },
    {
      "cell_type": "markdown",
      "source": "You can print out <b>predTree</b> and <b>y_testset</b> if you want to visually compare the predictions to the actual values.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "print (predTree [0:5])\nprint (y_testset [0:5])",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "['drugY' 'drugX' 'drugX' 'drugX' 'drugX']\n40     drugY\n51     drugX\n139    drugX\n197    drugX\n170    drugX\nName: Drug, dtype: object\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 19
    },
    {
      "cell_type": "markdown",
      "source": "<hr>\n\n<div id=\"evaluation\">\n    <h2>Evaluation</h2>\n    Next, let's import <b>metrics</b> from sklearn and check the accuracy of our model.\n</div>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn import metrics\nimport matplotlib.pyplot as plt\nprint(\"DecisionTrees's Accuracy: \", metrics.accuracy_score(y_testset, predTree))",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "DecisionTrees's Accuracy:  0.9833333333333333\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 21
    },
    {
      "cell_type": "markdown",
      "source": "__Accuracy classification score__ computes subset accuracy: the set of labels predicted for a sample must exactly match the corresponding set of labels in y_true.  \n\nIn multilabel classification, the function returns the subset accuracy. If the entire set of predicted labels for a sample strictly matches with the true set of labels, then the subset accuracy is 1.0; otherwise it is 0.0.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<hr>\n\n<div id=\"visualization\">\n    <h2>Visualization</h2>\n    \n    \nLet's visualize the tree\n</div>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Notice: You might need to uncomment and install the pydotplus and graphviz libraries if you have not installed these before\n#!conda install -c conda-forge pydotplus -y\n#!conda install -c conda-forge python-graphviz -y",
      "metadata": {},
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "source": "tree.plot_tree(drugTree)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 22
    }
  ]
}