Decision_Tree_Classifier.ipynb

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    "colab": {
      "name": "Decision Tree Classifier.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMZCr1UCpUCYD/riiix6xmu",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Shubhankar9934/ML_Machine_Learning_All_Algorithm/blob/main/Decision_Tree_Classifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "dbiHHJN8zi3m"
      },
      "outputs": [],
      "source": [
        "import pandas as pd \n",
        "import matplotlib.pyplot as plt \n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_iris"
      ],
      "metadata": {
        "id": "_QCBsroSz-tL"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "iris = load_iris()\n",
        "iris"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "At2CD5Ps0HxR",
        "outputId": "bf60b133-2efc-4247-b642-093d12b56767"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "{'DESCR': '.. _iris_dataset:\\n\\nIris plants dataset\\n--------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 150 (50 in each of three classes)\\n    :Number of Attributes: 4 numeric, predictive attributes and the class\\n    :Attribute Information:\\n        - sepal length in cm\\n        - sepal width in cm\\n        - petal length in cm\\n        - petal width in cm\\n        - class:\\n                - Iris-Setosa\\n                - Iris-Versicolour\\n                - Iris-Virginica\\n                \\n    :Summary Statistics:\\n\\n    ============== ==== ==== ======= ===== ====================\\n                    Min  Max   Mean    SD   Class Correlation\\n    ============== ==== ==== ======= ===== ====================\\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\n    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\\n    ============== ==== ==== ======= ===== ====================\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: 33.3% for each of 3 classes.\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\\nfrom Fisher\\'s paper. Note that it\\'s the same as in R, but not as in the UCI\\nMachine Learning Repository, which has two wrong data points.\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature.  Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant.  One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\n.. topic:: References\\n\\n   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n     Mathematical Statistics\" (John Wiley, NY, 1950).\\n   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n     Structure and Classification Rule for Recognition in Partially Exposed\\n     Environments\".  IEEE Transactions on Pattern Analysis and Machine\\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\\n     on Information Theory, May 1972, 431-433.\\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\\n     conceptual clustering system finds 3 classes in the data.\\n   - Many, many more ...',\n",
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              "        [6.2, 3.4, 5.4, 2.3],\n",
              "        [5.9, 3. , 5.1, 1.8]]),\n",
              " 'data_module': 'sklearn.datasets.data',\n",
              " 'feature_names': ['sepal length (cm)',\n",
              "  'sepal width (cm)',\n",
              "  'petal length (cm)',\n",
              "  'petal width (cm)'],\n",
              " 'filename': 'iris.csv',\n",
              " 'frame': None,\n",
              " 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]),\n",
              " 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10')}"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris.data\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kcm4c0SU0U02",
        "outputId": "e3e402c9-53e6-428b-fd5e-5bd98168af5e"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
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              "       [5.7, 3. , 4.2, 1.2],\n",
              "       [5.7, 2.9, 4.2, 1.3],\n",
              "       [6.2, 2.9, 4.3, 1.3],\n",
              "       [5.1, 2.5, 3. , 1.1],\n",
              "       [5.7, 2.8, 4.1, 1.3],\n",
              "       [6.3, 3.3, 6. , 2.5],\n",
              "       [5.8, 2.7, 5.1, 1.9],\n",
              "       [7.1, 3. , 5.9, 2.1],\n",
              "       [6.3, 2.9, 5.6, 1.8],\n",
              "       [6.5, 3. , 5.8, 2.2],\n",
              "       [7.6, 3. , 6.6, 2.1],\n",
              "       [4.9, 2.5, 4.5, 1.7],\n",
              "       [7.3, 2.9, 6.3, 1.8],\n",
              "       [6.7, 2.5, 5.8, 1.8],\n",
              "       [7.2, 3.6, 6.1, 2.5],\n",
              "       [6.5, 3.2, 5.1, 2. ],\n",
              "       [6.4, 2.7, 5.3, 1.9],\n",
              "       [6.8, 3. , 5.5, 2.1],\n",
              "       [5.7, 2.5, 5. , 2. ],\n",
              "       [5.8, 2.8, 5.1, 2.4],\n",
              "       [6.4, 3.2, 5.3, 2.3],\n",
              "       [6.5, 3. , 5.5, 1.8],\n",
              "       [7.7, 3.8, 6.7, 2.2],\n",
              "       [7.7, 2.6, 6.9, 2.3],\n",
              "       [6. , 2.2, 5. , 1.5],\n",
              "       [6.9, 3.2, 5.7, 2.3],\n",
              "       [5.6, 2.8, 4.9, 2. ],\n",
              "       [7.7, 2.8, 6.7, 2. ],\n",
              "       [6.3, 2.7, 4.9, 1.8],\n",
              "       [6.7, 3.3, 5.7, 2.1],\n",
              "       [7.2, 3.2, 6. , 1.8],\n",
              "       [6.2, 2.8, 4.8, 1.8],\n",
              "       [6.1, 3. , 4.9, 1.8],\n",
              "       [6.4, 2.8, 5.6, 2.1],\n",
              "       [7.2, 3. , 5.8, 1.6],\n",
              "       [7.4, 2.8, 6.1, 1.9],\n",
              "       [7.9, 3.8, 6.4, 2. ],\n",
              "       [6.4, 2.8, 5.6, 2.2],\n",
              "       [6.3, 2.8, 5.1, 1.5],\n",
              "       [6.1, 2.6, 5.6, 1.4],\n",
              "       [7.7, 3. , 6.1, 2.3],\n",
              "       [6.3, 3.4, 5.6, 2.4],\n",
              "       [6.4, 3.1, 5.5, 1.8],\n",
              "       [6. , 3. , 4.8, 1.8],\n",
              "       [6.9, 3.1, 5.4, 2.1],\n",
              "       [6.7, 3.1, 5.6, 2.4],\n",
              "       [6.9, 3.1, 5.1, 2.3],\n",
              "       [5.8, 2.7, 5.1, 1.9],\n",
              "       [6.8, 3.2, 5.9, 2.3],\n",
              "       [6.7, 3.3, 5.7, 2.5],\n",
              "       [6.7, 3. , 5.2, 2.3],\n",
              "       [6.3, 2.5, 5. , 1.9],\n",
              "       [6.5, 3. , 5.2, 2. ],\n",
              "       [6.2, 3.4, 5.4, 2.3],\n",
              "       [5.9, 3. , 5.1, 1.8]])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris.target"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7aCejrK_0fhe",
        "outputId": "4b593cb4-0196-488b-b8d8-dd683a6084b2"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris.feature_names\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HvFgBzO90jej",
        "outputId": "96cb0942-37b8-40f3-97d7-6a478b711c1c"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['sepal length (cm)',\n",
              " 'sepal width (cm)',\n",
              " 'petal length (cm)',\n",
              " 'petal width (cm)']"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "metadata": {
        "id": "MaCjVfUt0nDq"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classifier = DecisionTreeClassifier()"
      ],
      "metadata": {
        "id": "tLIPNLrt1Hkq"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import tree\n",
        "\n"
      ],
      "metadata": {
        "id": "uACyKc5K1dT5"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(16,9))\n",
        "classifier.fit(iris.data,iris.target)\n",
        "tree.plot_tree(classifier, filled = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 815
        },
        "id": "EaoIBj3a1jOA",
        "outputId": "5632dcc6-2620-4ce9-81a5-b1c44892dded"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 0.9166666666666666, 'X[3] <= 0.8\\ngini = 0.667\\nsamples = 150\\nvalue = [50, 50, 50]'),\n",
              " Text(0.4230769230769231, 0.75, 'gini = 0.0\\nsamples = 50\\nvalue = [50, 0, 0]'),\n",
              " Text(0.5769230769230769, 0.75, 'X[3] <= 1.75\\ngini = 0.5\\nsamples = 100\\nvalue = [0, 50, 50]'),\n",
              " Text(0.3076923076923077, 0.5833333333333334, 'X[2] <= 4.95\\ngini = 0.168\\nsamples = 54\\nvalue = [0, 49, 5]'),\n",
              " Text(0.15384615384615385, 0.4166666666666667, 'X[3] <= 1.65\\ngini = 0.041\\nsamples = 48\\nvalue = [0, 47, 1]'),\n",
              " Text(0.07692307692307693, 0.25, 'gini = 0.0\\nsamples = 47\\nvalue = [0, 47, 0]'),\n",
              " Text(0.23076923076923078, 0.25, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
              " Text(0.46153846153846156, 0.4166666666666667, 'X[3] <= 1.55\\ngini = 0.444\\nsamples = 6\\nvalue = [0, 2, 4]'),\n",
              " Text(0.38461538461538464, 0.25, 'gini = 0.0\\nsamples = 3\\nvalue = [0, 0, 3]'),\n",
              " Text(0.5384615384615384, 0.25, 'X[0] <= 6.95\\ngini = 0.444\\nsamples = 3\\nvalue = [0, 2, 1]'),\n",
              " Text(0.46153846153846156, 0.08333333333333333, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2, 0]'),\n",
              " Text(0.6153846153846154, 0.08333333333333333, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
              " Text(0.8461538461538461, 0.5833333333333334, 'X[2] <= 4.85\\ngini = 0.043\\nsamples = 46\\nvalue = [0, 1, 45]'),\n",
              " Text(0.7692307692307693, 0.4166666666666667, 'X[0] <= 5.95\\ngini = 0.444\\nsamples = 3\\nvalue = [0, 1, 2]'),\n",
              " Text(0.6923076923076923, 0.25, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
              " Text(0.8461538461538461, 0.25, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 0, 2]'),\n",
              " Text(0.9230769230769231, 0.4166666666666667, 'gini = 0.0\\nsamples = 43\\nvalue = [0, 0, 43]')]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x648 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Or3hIKQl15Ln"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}