{ "cells": [ { "cell_type": "markdown", "id": "excellent-tiger", "metadata": {}, "source": [ "## Regression with AdaBoost regressor" ] }, { "cell_type": "code", "execution_count": 1, "id": "refined-newsletter", "metadata": { "tags": [] }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import pandas as pd\n", "import numpy as np\n", "\n", "from sklearn import ensemble, datasets, metrics, model_selection" ] }, { "cell_type": "code", "execution_count": 2, "id": "cutting-miller", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ".. _boston_dataset:\n", "\n", "Boston house prices dataset\n", "---------------------------\n", "\n", "**Data Set Characteristics:** \n", "\n", " :Number of Instances: 506 \n", "\n", " :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\n", "\n", " :Attribute Information (in order):\n", " - CRIM per capita crime rate by town\n", " - ZN proportion of residential land zoned for lots over 25,000 sq.ft.\n", " - INDUS proportion of non-retail business acres per town\n", " - CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n", " - NOX nitric oxides concentration (parts per 10 million)\n", " - RM average number of rooms per dwelling\n", " - AGE proportion of owner-occupied units built prior to 1940\n", " - DIS weighted distances to five Boston employment centres\n", " - RAD index of accessibility to radial highways\n", " - TAX full-value property-tax rate per $10,000\n", " - PTRATIO pupil-teacher ratio by town\n", " - B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n", " - LSTAT % lower status of the population\n", " - MEDV Median value of owner-occupied homes in $1000's\n", "\n", " :Missing Attribute Values: None\n", "\n", " :Creator: Harrison, D. and Rubinfeld, D.L.\n", "\n", "This is a copy of UCI ML housing dataset.\n", "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n", "\n", "\n", "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n", "\n", "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n", "prices and the demand for clean air', J. Environ. Economics & Management,\n", "vol.5, 81-102, 1978. Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n", "...', Wiley, 1980. N.B. Various transformations are used in the table on\n", "pages 244-261 of the latter.\n", "\n", "The Boston house-price data has been used in many machine learning papers that address regression\n", "problems. \n", " \n", ".. topic:: References\n", "\n", " - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n", " - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n", "\n" ] } ], "source": [ "boston = datasets.load_boston()\n", "print(boston.DESCR)" ] }, { "cell_type": "code", "execution_count": 3, "id": "lesbian-blair", "metadata": { "tags": [] }, "outputs": [], "source": [ "X = pd.DataFrame(boston.data, columns=boston.feature_names)\n", "y = boston.target" ] }, { "cell_type": "code", "execution_count": 4, "id": "radical-folder", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "train samples: 354\n", "test samples 152\n" ] } ], "source": [ "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, train_size=0.7)\n", "\n", "print('train samples:', len(X_train))\n", "print('test samples', len(X_test))" ] }, { "cell_type": "code", "execution_count": 5, "id": "rural-batch", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 419.375x360 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df_train = pd.DataFrame(y_train, columns=['target'])\n", "df_train['type'] = 'train'\n", "\n", "df_test = pd.DataFrame(y_test, columns=['target'])\n", "df_test['type'] = 'test'\n", "\n", "df_set = df_train.append(df_test)\n", "\n", "_ = sns.displot(df_set, x=\"target\" ,hue=\"type\", kind=\"kde\", log_scale=False)" ] }, { "cell_type": "code", "execution_count": 6, "id": "surprised-poetry", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "AdaBoostRegressor()" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = ensemble.AdaBoostRegressor(n_estimators=50)\n", "model.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 7, "id": "educational-picture", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "predicted = model.predict(X_test)\n", "\n", "fig, ax = plt.subplots()\n", "ax.scatter(y_test, predicted)\n", "\n", "ax.set_xlabel('True Values')\n", "ax.set_ylabel('Predicted')\n", "_ = ax.plot([0, y.max()], [0, y.max()], ls='-', color='red')" ] }, { "cell_type": "code", "execution_count": 8, "id": "featured-blind", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "residual = y_test - predicted\n", "\n", "fig, ax = plt.subplots()\n", "ax.scatter(y_test, residual)\n", "ax.set_xlabel('y')\n", "ax.set_ylabel('residual')\n", "\n", "_ = plt.axhline(0, color='red', ls='--')" ] }, { "cell_type": "code", "execution_count": 9, "id": "brown-phone", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 360x360 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "_ = sns.displot(residual, kind=\"kde\");" ] }, { "cell_type": "code", "execution_count": 10, "id": "convinced-royal", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "r2 score: 0.8447726177620123\n", "mse: 13.637542247084053\n", "rmse: 3.692904310577794\n", "mae: 2.793980126322056\n" ] } ], "source": [ "print(\"r2 score: {}\".format(metrics.r2_score(y_test, predicted)))\n", "print(\"mse: {}\".format(metrics.mean_squared_error(y_test, predicted)))\n", "print(\"rmse: {}\".format(np.sqrt(metrics.mean_squared_error(y_test, predicted))))\n", "print(\"mae: {}\".format(metrics.mean_absolute_error(y_test, predicted)))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 5 }