59 lines
1.8 KiB
Python
59 lines
1.8 KiB
Python
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"""
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============================
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Gradient Boosting regression
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============================
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Demonstrate Gradient Boosting on the boston housing dataset.
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This example fits a Gradient Boosting model with least squares loss and
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100 regression trees of depth 4.
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"""
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print __doc__
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# Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
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#
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# License: BSD
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import numpy as np
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import pylab as pl
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from sklearn import ensemble
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from sklearn import datasets
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from sklearn.utils import shuffle
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from sklearn import cross_validation
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###############################################################################
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# Load data
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boston = datasets.load_boston()
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X, y = shuffle(boston.data, boston.target, random_state=13)
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offset = int(X.shape[0] * 0.9)
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X_train, y_train = X[:offset], y[:offset]
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X_test, y_test = X[offset:], y[offset:]
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################################################################################
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# Fit regression model
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params = {'n_iter': 100, 'max_depth': 4, 'min_split': 1, 'learn_rate': 0.1,
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'loss': 'ls'}
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clf = ensemble.GradientBoostingRegressor(**params)
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clf.fit(X_train, y_train)
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mse = np.mean((clf.predict(X_test) - y_test) ** 2.0)
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pl.figure(figsize=(12, 6))
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pl.subplot(1,2,1)
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pl.title('Deviance')
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pl.plot(np.arange(params['n_iter']) + 1, clf.train_deviance, "r-")
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pl.xlabel('Boosting Iterations')
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pl.ylabel('Training Set Deviance')
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################################################################################
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# get variable importance
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variable_importance = clf.variable_importance
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sorted_idx = np.argsort(variable_importance)
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pos = np.arange(sorted_idx.shape[0]) + .5
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pl.subplot(1,2,2)
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pl.barh(pos, variable_importance[sorted_idx], align='center')
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pl.yticks(pos, boston.feature_names[sorted_idx])
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pl.xlabel('Relative importance')
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pl.title('Relative importance of the features for the Boston housing data.')
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pl.show()
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