70 lines
1.9 KiB
Python
70 lines
1.9 KiB
Python
"""
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========================================
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Testing and Training Error with Boosting
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========================================
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This example shows the use of boosting to improve prediction accuracy.
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The error on the test and training sets after each boost is plotted on
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the left. The boost weights and error of each tree are also shown.
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"""
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print __doc__
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from itertools import izip
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import pylab as pl
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from sklearn.ensemble import AdaBoostClassifier
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.datasets.samples_generator import make_gaussian_quantiles
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X, y = make_gaussian_quantiles(n_samples=2000, n_features=10,
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n_classes=3)
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n_split = 1000
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X_train, X_test = X[:n_split], X[n_split:]
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y_train, y_test = y[:n_split], y[n_split:]
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test_errors = []
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train_errors = []
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bdt = AdaBoostClassifier(DecisionTreeClassifier(min_samples_leaf=100),
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n_estimators=100, learn_rate=.05)
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bdt.fit(X_train, y_train)
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for y_test_predict, y_train_predict in izip(bdt.staged_predict(X_test),
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bdt.staged_predict(X_train)):
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test_errors.append((y_test_predict != y_test).sum() / float(y_test.shape[0]))
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train_errors.append((y_train_predict != y_train).sum() / float(y_train.shape[0]))
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n_trees = xrange(1, len(bdt) + 1)
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pl.figure(figsize=(15, 5))
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pl.subplot(1, 3, 1)
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pl.plot(n_trees, test_errors, "b", label='test')
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pl.plot(n_trees, train_errors, "r", label='train')
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pl.legend()
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pl.ylabel('Error')
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pl.xlabel('Number of Trees')
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pl.subplot(1, 3, 2)
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pl.plot(n_trees, bdt.errs_, "b")
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pl.ylabel('Error')
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pl.xlabel('Tree')
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pl.ylim((.2, max(bdt.errs_) * 1.2))
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pl.xlim((-20, len(bdt) + 20))
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pl.subplot(1, 3, 3)
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pl.plot(n_trees, bdt.boost_weights_, "b")
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pl.ylabel('Boost Weight')
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pl.xlabel('Tree')
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pl.ylim((0, max(bdt.boost_weights_) * 1.2))
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pl.xlim((-20, len(bdt) + 20))
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# prevent overlapping y-axis labels
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pl.subplots_adjust(wspace=0.4)
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pl.show()
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