52 lines
1.4 KiB
Python
52 lines
1.4 KiB
Python
"""
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======================================
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Decision Tree Regression with AdaBoost
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======================================
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A decision tree is boosted using the AdaBoost.R2 [1] algorithm on a 1D
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sinusoidal dataset with a small amount of Gaussian noise.
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299 boosts (300 decision trees) is compared with a single decision tree
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regressor. As the number of boosts is increased the regressor can fit more
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detail.
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.. [1] H. Drucker, "Improving Regressors using Boosting Techniques", 1997.
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"""
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print(__doc__)
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import numpy as np
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# Create a the dataset
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rng = np.random.RandomState(1)
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X = np.linspace(0, 6, 100)[:, np.newaxis]
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y = np.sin(X).ravel() + np.sin(6 * X).ravel() + rng.normal(0, 0.1, X.shape[0])
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# Fit regression model
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from sklearn.tree import DecisionTreeRegressor
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from sklearn.ensemble import AdaBoostRegressor
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clf_1 = DecisionTreeRegressor(max_depth=4)
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clf_2 = AdaBoostRegressor(DecisionTreeRegressor(max_depth=4),
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n_estimators=300, random_state=rng)
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clf_1.fit(X, y)
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clf_2.fit(X, y)
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# Predict
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y_1 = clf_1.predict(X)
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y_2 = clf_2.predict(X)
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# Plot the results
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import pylab as pl
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pl.figure()
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pl.scatter(X, y, c="k", label="training samples")
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pl.plot(X, y_1, c="g", label="n_estimators=1", linewidth=2)
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pl.plot(X, y_2, c="r", label="n_estimators=300", linewidth=2)
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pl.xlabel("data")
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pl.ylabel("target")
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pl.title("Boosted Decision Tree Regression")
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pl.legend()
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pl.show()
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