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

    Click :ref:`here <sphx_glr_download_auto_examples_ensemble_plot_adaboost_regression.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_ensemble_plot_adaboost_regression.py:


======================================
Decision Tree Regression with AdaBoost
======================================

A decision tree is boosted using the AdaBoost.R2 [1]_ algorithm on a 1D
sinusoidal dataset with a small amount of Gaussian noise.
299 boosts (300 decision trees) is compared with a single decision tree
regressor. As the number of boosts is increased the regressor can fit more
detail.

.. [1] H. Drucker, "Improving Regressors using Boosting Techniques", 1997.





.. image:: /auto_examples/ensemble/images/sphx_glr_plot_adaboost_regression_001.png
    :class: sphx-glr-single-img





.. code-block:: python

    print(__doc__)

    # Author: Noel Dawe <noel.dawe@gmail.com>
    #
    # License: BSD 3 clause

    # importing necessary libraries
    import numpy as np
    import matplotlib.pyplot as plt
    from sklearn.tree import DecisionTreeRegressor
    from sklearn.ensemble import AdaBoostRegressor

    # Create the dataset
    rng = np.random.RandomState(1)
    X = np.linspace(0, 6, 100)[:, np.newaxis]
    y = np.sin(X).ravel() + np.sin(6 * X).ravel() + rng.normal(0, 0.1, X.shape[0])

    # Fit regression model
    regr_1 = DecisionTreeRegressor(max_depth=4)

    regr_2 = AdaBoostRegressor(DecisionTreeRegressor(max_depth=4),
                              n_estimators=300, random_state=rng)

    regr_1.fit(X, y)
    regr_2.fit(X, y)

    # Predict
    y_1 = regr_1.predict(X)
    y_2 = regr_2.predict(X)

    # Plot the results
    plt.figure()
    plt.scatter(X, y, c="k", label="training samples")
    plt.plot(X, y_1, c="g", label="n_estimators=1", linewidth=2)
    plt.plot(X, y_2, c="r", label="n_estimators=300", linewidth=2)
    plt.xlabel("data")
    plt.ylabel("target")
    plt.title("Boosted Decision Tree Regression")
    plt.legend()
    plt.show()

**Total running time of the script:** ( 0 minutes  0.226 seconds)


.. _sphx_glr_download_auto_examples_ensemble_plot_adaboost_regression.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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