50 lines
1.5 KiB
Python
50 lines
1.5 KiB
Python
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"""
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===================================================================
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Decision Tree Regression
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===================================================================
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A 1D regression with decision tree.
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The :ref:`decision trees <tree>` is
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used to fit a sine curve with addition noisy observation. As a result, it
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learns local linear regressions approximating the sine curve.
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We can see that if the maximum depth of the tree (controlled by the
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`max_depth` parameter) is set too 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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# Import the necessary modules and libraries
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.tree import DecisionTreeRegressor
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# Create a random dataset
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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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y[::5] += 3 * (0.5 - rng.rand(16))
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# Fit regression model
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regr_1 = DecisionTreeRegressor(max_depth=2)
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regr_2 = DecisionTreeRegressor(max_depth=5)
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regr_1.fit(X, y)
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regr_2.fit(X, y)
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# Predict
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X_test = np.arange(0.0, 5.0, 0.01)[:, np.newaxis]
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y_1 = regr_1.predict(X_test)
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y_2 = regr_2.predict(X_test)
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# Plot the results
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plt.figure()
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plt.scatter(X, y, s=20, edgecolor="black", c="darkorange", label="data")
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plt.plot(X_test, y_1, color="cornflowerblue", label="max_depth=2", linewidth=2)
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plt.plot(X_test, y_2, color="yellowgreen", label="max_depth=5", linewidth=2)
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plt.xlabel("data")
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plt.ylabel("target")
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plt.title("Decision Tree Regression")
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plt.legend()
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plt.show()
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