52 lines
1.7 KiB
Python
52 lines
1.7 KiB
Python
"""
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===================================================================
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Decision Tree Regression
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===================================================================
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1D regression with :ref:`decision trees <tree>`: the decision tree is
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used to fit a sine curve with addition noisy observation. As a result, it
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learn local linear regressions approximating the sine curve.
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We can see that if the maximum depth of the tree (controled by the
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`max_depth` parameter) is set to high, the decision trees learn too fine
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details of the training data and learn from the noise, i.e. they overfit.
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"""
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print __doc__
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###############################################################################
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# Generate sample data
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import numpy as np
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# Create a random number generator
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rng = np.random.RandomState(1)
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X = np.sort(5*rng.rand(80, 1), axis=0)
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y = np.sin(X).ravel()
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###############################################################################
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# Add noise to targets
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y[::5] += 3*(0.5 - rng.rand(16))
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###############################################################################
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# Fit regression model
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from sklearn.tree import DecisionTreeRegressor
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clf_1 = DecisionTreeRegressor(max_depth=2)
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clf_2 = DecisionTreeRegressor(max_depth=5)
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y_1 = clf_1.fit(X, y).predict(X)
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y_2 = clf_2.fit(X, y).predict(X)
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###############################################################################
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# look at the results
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import pylab as pl
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pl.figure(1, figsize=(5, 4))
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pl.clf()
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pl.scatter(X, y, c='k', label='data')
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pl.plot(X, y_1, c='g', label='max_depth=2', linewidth=2)
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pl.plot(X, y_2, c='r', label='max_depth=5', linewidth=2)
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pl.axis('tight')
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pl.xlabel('data')
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pl.ylabel('target')
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pl.title('Decision Tree Regression')
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pl.legend(loc='best')
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pl.show()
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