A 1D regression with decision tree.
The decision trees is used to fit a sine curve with addition noisy observation. As a result, it learns local linear regressions approximating the sine curve.
We can see that if the maximum depth of the tree (controlled by the max_depth parameter) is set too high, the decision trees learn too fine details of the training data and learn from the noise, i.e. they overfit.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | print(__doc__)# Import the necessary modules and librariesimport numpy as npfrom sklearn.tree import DecisionTreeRegressorimport matplotlib.pyplot as plt# Create a random datasetrng = np.random.RandomState(1)X = np.sort(5 * rng.rand(80, 1), axis=0)y = np.sin(X).ravel()y[::5] += 3 * (0.5 - rng.rand(16))# Fit regression modelregr_1 = DecisionTreeRegressor(max_depth=2)regr_2 = DecisionTreeRegressor(max_depth=5)regr_1.fit(X, y)regr_2.fit(X, y)# PredictX_test = np.arange(0.0, 5.0, 0.01)[:, np.newaxis]y_1 = regr_1.predict(X_test)y_2 = regr_2.predict(X_test)# Plot the resultsplt.figure()plt.scatter(X, y, c="darkorange", label="data")plt.plot(X_test, y_1, color="cornflowerblue", label="max_depth=2", linewidth=2)plt.plot(X_test, y_2, color="yellowgreen", label="max_depth=5", linewidth=2)plt.xlabel("data")plt.ylabel("target")plt.title("Decision Tree Regression")plt.legend()plt.show() |
Total running time of the script: (0 minutes 0.060 seconds)
Download Python source code:
plot_tree_regression.py
Download IPython notebook:
plot_tree_regression.ipynb
Please login to continue.