.. _sphx_glr_auto_examples_tree_plot_tree_regression.py:


===================================================================
Decision Tree Regression
===================================================================

A 1D regression with decision tree.

The :ref:`decision trees <tree>` 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, 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)



.. container:: sphx-glr-download

    **Download Python source code:** :download:`plot_tree_regression.py <plot_tree_regression.py>`


.. container:: sphx-glr-download

    **Download IPython notebook:** :download:`plot_tree_regression.ipynb <plot_tree_regression.ipynb>`