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arghyadeep99/decision-trees-implementation.ipynb

Created August 16, 2020 12:56
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Decision Trees Implementation.ipynb
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decision-trees-implementation.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Decision Trees Implementation.ipynb",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/arghyadeep99/8467a58c08e47070b80f926861b20eee/decision-trees-implementation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "I6qKGWQz4i3b",
"colab_type": "text"
},
"source": [
"# Decision Trees\n",
"“The possible solutions to a given problem emerge as the leaves of a tree, each node representing a point of deliberation and decision.”\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fdeQCp-yHtL7",
"colab_type": "text"
},
"source": [
"To understand how Decision Trees are like sophisticated \"if-else\", click [here](https://stackoverflow.com/questions/20224526/how-to-extract-the-decision-rules-from-scikit-learn-decision-tree)."
]
},
{
"cell_type": "code",
"metadata": {
"id": "EYd2O_Lm5BcF",
"colab_type": "code",
"colab": {}
},
"source": [
"from sklearn import datasets\n",
"from sklearn.tree import DecisionTreeClassifier"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "DdxxbmYG5JyE",
"colab_type": "code",
"colab": {}
},
"source": [
"iris = datasets.load_iris()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "0HuImXCiaEZK",
"colab_type": "code",
"colab": {}
},
"source": [
"x_setosa = iris['data'][:50, (2)]\n",
"y_setosa = iris['data'][:50, (3)]\n",
"x_versicolor = iris['data'][50:100, (2)]\n",
"y_versicolor = iris['data'][50:100, (3)]\n",
"x_virginica = iris['data'][100:150, (2)]\n",
"y_virginica = iris['data'][100:150, (3)]\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "mlOnK2fMcaoo",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 298
},
"outputId": "959d86a3-5708-499e-a793-cc9f28013289"
},
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"plt.xlabel('petal_length')\n",
"plt.ylabel('petal_width')\n",
"plt.plot(x_setosa,y_setosa, 'ro') #plotting setosa flower\n",
"plt.plot(x_versicolor,y_versicolor, 'go') #plotting versicolor flower\n",
"plt.plot(x_virginica,y_virginica, 'yo')"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f0129fca2e8>]"
]
},
"metadata": {
"tags": []
},
"execution_count": 6
},
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oe45ucCrzott",
"colab_type": "text"
},
"source": [
"**Decision Boundary**\n",
"\n",
"Decision Trees divide the input space into axis-parallel rectangles\n",
"and label each rectangle with one of the K classes\n",
"\n",
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)\n",
"\n",
"[Decision Boundaries for iris Sklearn dataset](https://scikit-learn.org/stable/auto_examples/tree/plot_iris_dtc.html)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "wAVSZkpezhpa",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 315
},
"outputId": "34e79ae0-9ead-41b5-ec0c-4ca555ef6972"
},
"source": [
"print('Decision Boundary')\n",
"plt.xlabel('petal_length')\n",
"plt.ylabel('petal_width')\n",
"plt.plot(x_setosa,y_setosa, 'ro') #plotting setosa flower\n",
"plt.plot(x_versicolor,y_versicolor, 'go') #plotting versicolor flower\n",
"plt.plot(x_virginica,y_virginica, 'yo')\n",
"plt.plot([0,7], [0.8, 0.8], 'k-', lw=2)\n",
"plt.plot([0,7], [1.75, 1.75], 'k-', lw=2)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Decision Boundary\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f01299cb630>]"
]
},
"metadata": {
"tags": []
},
"execution_count": 11
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "6eb3gp-qgLJy",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 88
},
"outputId": "7e06c850-e2e7-4ab1-945b-eede9b61e8fe"
},
"source": [
"iris.feature_names"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['sepal length (cm)',\n",
" 'sepal width (cm)',\n",
" 'petal length (cm)',\n",
" 'petal width (cm)']"
]
},
"metadata": {
"tags": []
},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "gtKZzGge5OxQ",
"colab_type": "code",
"colab": {}
},
"source": [
"X = iris.data[:,2:]\n",
"y = iris.target"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "APiN_J_-5cL7",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 124
},
"outputId": "9f8234e1-3e2d-400a-e7a3-ab335ecb6aae"
},
"source": [
"tree_clf = DecisionTreeClassifier(max_depth=2)\n",
"tree_clf.fit(X, y)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
" max_depth=2, max_features=None, max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, presort='deprecated',\n",
" random_state=None, splitter='best')"
]
},
"metadata": {
"tags": []
},
"execution_count": 10
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MVRLrlcj34rj",
"colab_type": "text"
},
"source": [
"**Task For You**\n",
"\n",
"Implement Decision Trees from scratch using either gini index or entropy as impurity metric"
]
}
]
}
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