{ "cells": [ { "cell_type": "markdown", "id": "bronze-conservation", "metadata": {}, "source": [ "## Regression with random forest" ] }, { "cell_type": "code", "execution_count": 1, "id": "timely-coast", "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": "irish-transformation", "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": "desperate-alabama", "metadata": { "tags": [] }, "outputs": [], "source": [ "X = pd.DataFrame(boston.data, columns=boston.feature_names)\n", "y = boston.target" ] }, { "cell_type": "code", "execution_count": 4, "id": "liberal-roulette", "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": "physical-journalism", "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": "atlantic-refrigerator", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "RandomForestRegressor(max_depth=4)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = ensemble.RandomForestRegressor(n_estimators=100, max_depth=4, criterion='mse')\n", "model.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 7, "id": "experienced-basin", "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": "attached-optics", "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": "divine-politics", "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": "cardiovascular-karen", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "r2 score: 0.8880432449475412\n", "mse: 9.566086592342264\n", "rmse: 3.0929090824565577\n", "mae: 2.4799653525378056\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 }