ML0101EN_Clas_Decision_Trees_drug.ipynb

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      "source": [
        "<p style=\"text-align:center\">\n",
        "    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\">\n",
        "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n",
        "    </a>\n",
        "</p>\n",
        "\n",
        "# Decision Trees\n",
        "\n",
        "Estimated time needed: **15** minutes\n",
        "\n",
        "## Objectives\n",
        "\n",
        "After completing this lab you will be able to:\n",
        "\n",
        "*   Develop a classification model using Decision Tree Algorithm\n"
      ]
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      "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"
      ]
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      "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=\"https://#about_dataset\">About the dataset</a></li>\n",
        "        <li><a href=\"https://#downloading_data\">Downloading the Data</a></li>\n",
        "        <li><a href=\"https://#pre-processing\">Pre-processing</a></li>\n",
        "        <li><a href=\"https://#setting_up_tree\">Setting up the Decision Tree</a></li>\n",
        "        <li><a href=\"https://#modeling\">Modeling</a></li>\n",
        "        <li><a href=\"https://#prediction\">Prediction</a></li>\n",
        "        <li><a href=\"https://#evaluation\">Evaluation</a></li>\n",
        "        <li><a href=\"https://#visualization\">Visualization</a></li>\n",
        "    </ol>\n",
        "</div>\n",
        "<br>\n",
        "<hr>\n"
      ]
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      "source": [
        "Import the Following Libraries:\n",
        "\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"
      ]
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      "source": [
        "if you uisng you own version comment out\n"
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      "source": [
        "# Surpress warnings:\n",
        "def warn(*args, **kwargs):\n",
        "    pass\n",
        "import warnings\n",
        "warnings.warn = warn"
      ]
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      "source": [
        "import sys\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "import sklearn.tree as tree"
      ]
    },
    {
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      "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"
      ]
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      "source": [
        "<div id=\"downloading_data\">\n",
        "    <h2>Downloading the Data</h2>\n",
        "    To download the data, we will use pandas library to read itdirectly into a dataframe from IBM Object Storage.\n",
        "</div>\n"
      ]
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              "   Age Sex      BP Cholesterol  Na_to_K   Drug\n",
              "0   23   F    HIGH        HIGH   25.355  drugY\n",
              "1   47   M     LOW        HIGH   13.093  drugC\n",
              "2   47   M     LOW        HIGH   10.114  drugC\n",
              "3   28   F  NORMAL        HIGH    7.798  drugX\n",
              "4   61   F     LOW        HIGH   18.043  drugY"
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          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "my_data = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/drug200.csv', delimiter=\",\")\n",
        "my_data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bC2a1xaRprCd"
      },
      "source": [
        "**Did you know?** When it comes to Machine Learning, you will likely be working with large datasets. As a business, where can you host your data? IBM is offering a unique opportunity for businesses, with 10 Tb of IBM Cloud Object Storage: [Sign up now for free](http://cocl.us/ML0101EN-IBM-Offer-CC)\n"
      ]
    },
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        "run_control": {
          "read_only": false
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        "id": "Q4Tyfc7XprCe"
      },
      "source": [
        "<div id=\"practice\">\n",
        "    <h3>Practice</h3>\n",
        "    What is the size of data?\n",
        "</div>\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
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        "id": "TbnOeLnSprCe",
        "outputId": "2ee308ec-4c71-427a-dcc5-27847c5cb96b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "# write your code here\n",
        "\n",
        "my_data.shape\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VwSpJ2ELprCe"
      },
      "source": [
        "<details><summary>Click here for the solution</summary>\n",
        "\n",
        "```python\n",
        "my_data.shape\n",
        "\n",
        "```\n",
        "\n",
        "</details>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r8qFriyIprCe"
      },
      "source": [
        "<div href=\"pre-processing\">\n",
        "    <h2>Pre-processing</h2>\n",
        "</div>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "pVSnbxWBprCe"
      },
      "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"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "iWXvOtFmprCe"
      },
      "source": [
        "Remove the column containing the target name since it doesn't contain numeric values.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
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        "id": "UmQhVbsUprCe",
        "outputId": "4d6db086-09c8-440e-b9b1-c4dfa20862de"
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      "outputs": [
        {
          "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": 6
        }
      ],
      "source": [
        "X = my_data[['Age', 'Sex', 'BP', 'Cholesterol', 'Na_to_K']].values\n",
        "X[0:5]\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "emYjWaKLprCe"
      },
      "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 **pandas.get_dummies()**\n",
        "to convert the categorical variable into dummy/indicator variables.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
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        },
        "id": "m89ekq3YprCe",
        "outputId": "2d491e01-3ef3-46c1-f0ce-ebe3377007d3"
      },
      "outputs": [
        {
          "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": 7
        }
      ],
      "source": [
        "from sklearn import preprocessing\n",
        "le_sex = preprocessing.LabelEncoder()\n",
        "le_sex.fit(['F','M'])\n",
        "X[:,1] = le_sex.transform(X[:,1])\n",
        "\n",
        "\n",
        "le_BP = preprocessing.LabelEncoder()\n",
        "le_BP.fit([ 'LOW', 'NORMAL', 'HIGH'])\n",
        "X[:,2] = le_BP.transform(X[:,2])\n",
        "\n",
        "\n",
        "le_Chol = preprocessing.LabelEncoder()\n",
        "le_Chol.fit([ 'NORMAL', 'HIGH'])\n",
        "X[:,3] = le_Chol.transform(X[:,3])\n",
        "\n",
        "X[0:5]\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nRm2Z5d9prCe"
      },
      "source": [
        "Now we can fill the target variable.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1jOZBzPoprCf",
        "outputId": "45f53c3f-a08e-4796-c0ef-31257faadef9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    drugY\n",
              "1    drugC\n",
              "2    drugC\n",
              "3    drugX\n",
              "4    drugY\n",
              "Name: Drug, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "y = my_data[\"Drug\"]\n",
        "y[0:5]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "Fk78_wpzprCf"
      },
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "akI9oS1dprCf"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "QVaWQgj1prCf"
      },
      "source": [
        "Now <b> train_test_split </b> will return 4 different parameters. We will name them:<br>\n",
        "X_trainset, X_testset, y_trainset, y_testset <br> <br>\n",
        "The <b> train_test_split </b> will need the parameters: <br>\n",
        "X, y, test_size=0.3, and random_state=3. <br> <br>\n",
        "The <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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "k2R6IYXgprCf"
      },
      "outputs": [],
      "source": [
        "X_trainset, X_testset, y_trainset, y_testset = train_test_split(X, y, test_size=0.3, random_state=3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "K8aUKeBTprCf"
      },
      "source": [
        "<h3>Practice</h3>\n",
        "Print the shape of X_trainset and y_trainset. Ensure that the dimensions match.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d_Cu3zg_prCf",
        "outputId": "8b7d45ba-5aa5-4754-ed9a-ae73319ef3cc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of X training set (140, 5) &  Size of Y training set (140,)\n"
          ]
        }
      ],
      "source": [
        "# your code\n",
        "print('Shape of X training set {}'.format(X_trainset.shape),'&',' Size of Y training set {}'.format(y_trainset.shape))\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9FN0FQHNprCf"
      },
      "source": [
        "<details><summary>Click here for the solution</summary>\n",
        "\n",
        "```python\n",
        "print('Shape of X training set {}'.format(X_trainset.shape),'&',' Size of Y training set {}'.format(y_trainset.shape))\n",
        "\n",
        "```\n",
        "\n",
        "</details>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "Y9hRZSNxprCf"
      },
      "source": [
        "Print the shape of X_testset and y_testset. Ensure that the dimensions match.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "Lp8PBxJ6prCg"
      },
      "outputs": [],
      "source": [
        "# your code\n",
        "print('Shape of X training set {}'.format(X_testset.shape),'&',' Size of Y training set {}'.format(y_testset.shape))\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v5DhBuMaprCg"
      },
      "source": [
        "<details><summary>Click here for the solution</summary>\n",
        "\n",
        "```python\n",
        "print('Shape of X training set {}'.format(X_testset.shape),'&',' Size of Y training set {}'.format(y_testset.shape))\n",
        "\n",
        "```\n",
        "\n",
        "</details>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "wml80jP8prCg"
      },
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "UGrsr6dsprCg",
        "outputId": "f84b47b7-c998-4ec3-f2da-266abf833fe9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(criterion='entropy', max_depth=4)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</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\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=4)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "drugTree = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 4)\n",
        "drugTree # it shows the default parameters"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "y9PrSrPgprCg"
      },
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "jvumktE9prCg",
        "outputId": "099907ea-a9bf-422f-96f7-ee636577a7c0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(criterion='entropy', max_depth=4)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</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 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 sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=4)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "drugTree.fit(X_trainset,y_trainset)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "Hl3EVG-yprCg"
      },
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "s2Xofw3WprCg"
      },
      "outputs": [],
      "source": [
        "predTree = drugTree.predict(X_testset)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "viu2e_rYprCg"
      },
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L6wq5Zp3prCn",
        "outputId": "e484b3cf-00be-4c0c-e96a-3c655ab89a88"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['drugY' 'drugX' 'drugX' 'drugX' 'drugX']\n",
            "40     drugY\n",
            "51     drugX\n",
            "139    drugX\n",
            "197    drugX\n",
            "170    drugX\n",
            "Name: Drug, dtype: object\n"
          ]
        }
      ],
      "source": [
        "print (predTree [0:5])\n",
        "print (y_testset [0:5])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "kED0c8JFprCn"
      },
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gW-HYSBRprCo",
        "outputId": "dbd0b1a7-ec78-488d-ee3e-eabc049bf3d5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DecisionTrees's Accuracy:  0.9833333333333333\n"
          ]
        }
      ],
      "source": [
        "from sklearn import metrics\n",
        "import matplotlib.pyplot as plt\n",
        "print(\"DecisionTrees's Accuracy: \", metrics.accuracy_score(y_testset, predTree))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "Je5NTWOmprCo"
      },
      "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",
        "\n",
        "In 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"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dKX6DLSoprCs"
      },
      "source": [
        "<hr>\n",
        "\n",
        "<div id=\"visualization\">\n",
        "    <h2>Visualization</h2>\n",
        "\n",
        "Let's visualize the tree\n",
        "\n",
        "</div>\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "svBHTlJCprCs"
      },
      "outputs": [],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "id": "XjCV-ZgYprCs",
        "outputId": "93a6f5ef-4455-4053-c730-52c267e6387d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "tree.plot_tree(drugTree)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "equ9GBqZprCs"
      },
      "source": [
        "<h2>Want to learn more?</h2>\n",
        "\n",
        "IBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: <a href=\"https://www.ibm.com/analytics/spss-statistics-software?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\">SPSS Modeler</a>\n",
        "\n",
        "Also, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at <a href=\"https://www.ibm.com/cloud/watson-studio?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\">Watson Studio</a>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JNVxVsNpprCs"
      },
      "source": [
        "### Thank you for completing this lab!\n",
        "\n",
        "## Author\n",
        "\n",
        "Saeed Aghabozorgi\n",
        "\n",
        "### Other Contributors\n",
        "\n",
        "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\">Joseph Santarcangelo</a>\n",
        "\n",
        "<a href=\"https://www.linkedin.com/in/richard-ye/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\">Richard Ye</a>\n",
        "\n",
        "## Change Log\n",
        "\n",
        "| Date (YYYY-MM-DD) | Version | Changed By | Change Description                               |\n",
        "| ----------------- | ------- | ---------- | ------------------------------------------------ |\n",
        "| 2022-05-24        | 2.3     | Richard Ye | Fixed ability to work in JupyterLite and locally |\n",
        "| 2020-11-20        | 2.2     | Lakshmi    | Changed import statement of StringIO             |\n",
        "| 2020-11-03        | 2.1     | Lakshmi    | Changed URL of the csv                           |\n",
        "| 2020-08-27        | 2.0     | Lavanya    | Moved lab to course repo in GitLab               |\n",
        "|                   |         |            |                                                  |\n",
        "|                   |         |            |                                                  |\n",
        "\n",
        "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python",
      "language": "python",
      "name": "conda-env-python-py"
    },
    "language_info": {
      "name": ""
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}