.. _sphx_glr_auto_examples_tree_plot_tree_regression.py: =================================================================== Decision Tree Regression =================================================================== A 1D regression with decision tree. The :ref:`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. .. image:: /auto_examples/tree/images/sphx_glr_plot_tree_regression_001.png :align: center .. code-block:: python print(__doc__) # Import the necessary modules and libraries import numpy as np from sklearn.tree import DecisionTreeRegressor import matplotlib.pyplot as plt # Create a random dataset rng = 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 model regr_1 = DecisionTreeRegressor(max_depth=2) regr_2 = DecisionTreeRegressor(max_depth=5) regr_1.fit(X, y) regr_2.fit(X, y) # Predict X_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 results plt.figure() plt.scatter(X, y, s=20, edgecolor="black", 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 0.022 seconds) .. container:: sphx-glr-footer .. container:: sphx-glr-download :download:`Download Python source code: plot_tree_regression.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: plot_tree_regression.ipynb ` .. rst-class:: sphx-glr-signature `Generated by Sphinx-Gallery `_