
.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/tree/plot_tree_regression.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_tree_plot_tree_regression.py>`
        to download the full example code or to run this example in your browser via Binder

.. rst-class:: sphx-glr-example-title

.. _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.

.. GENERATED FROM PYTHON SOURCE LINES 16-50



.. image-sg:: /auto_examples/tree/images/sphx_glr_plot_tree_regression_001.png
   :alt: Decision Tree Regression
   :srcset: /auto_examples/tree/images/sphx_glr_plot_tree_regression_001.png
   :class: sphx-glr-single-img





.. code-block:: default


    # Import the necessary modules and libraries
    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn.tree import DecisionTreeRegressor

    # 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()


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.056 seconds)


.. _sphx_glr_download_auto_examples_tree_plot_tree_regression.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example


    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.3.X?urlpath=lab/tree/notebooks/auto_examples/tree/plot_tree_regression.ipynb
        :alt: Launch binder
        :width: 150 px



    .. container:: sphx-glr-download sphx-glr-download-python

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

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_tree_regression.ipynb <plot_tree_regression.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
