.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/ensemble/plot_gradient_boosting_regularization.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_ensemble_plot_gradient_boosting_regularization.py>`
        to download the full example code. or to run this example in your browser via JupyterLite or Binder

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

.. _sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regularization.py:


================================
Gradient Boosting regularization
================================

Illustration of the effect of different regularization strategies
for Gradient Boosting. The example is taken from Hastie et al 2009 [1]_.

The loss function used is binomial deviance. Regularization via
shrinkage (``learning_rate < 1.0``) improves performance considerably.
In combination with shrinkage, stochastic gradient boosting
(``subsample < 1.0``) can produce more accurate models by reducing the
variance via bagging.
Subsampling without shrinkage usually does poorly.
Another strategy to reduce the variance is by subsampling the features
analogous to the random splits in Random Forests
(via the ``max_features`` parameter).

.. [1] T. Hastie, R. Tibshirani and J. Friedman, "Elements of Statistical
    Learning Ed. 2", Springer, 2009.

.. GENERATED FROM PYTHON SOURCE LINES 23-91



.. image-sg:: /auto_examples/ensemble/images/sphx_glr_plot_gradient_boosting_regularization_001.png
   :alt: plot gradient boosting regularization
   :srcset: /auto_examples/ensemble/images/sphx_glr_plot_gradient_boosting_regularization_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    # Authors: The scikit-learn developers
    # SPDX-License-Identifier: BSD-3-Clause

    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn import datasets, ensemble
    from sklearn.metrics import log_loss
    from sklearn.model_selection import train_test_split

    X, y = datasets.make_hastie_10_2(n_samples=4000, random_state=1)

    # map labels from {-1, 1} to {0, 1}
    labels, y = np.unique(y, return_inverse=True)

    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.8, random_state=0)

    original_params = {
        "n_estimators": 400,
        "max_leaf_nodes": 4,
        "max_depth": None,
        "random_state": 2,
        "min_samples_split": 5,
    }

    plt.figure()

    for label, color, setting in [
        ("No shrinkage", "orange", {"learning_rate": 1.0, "subsample": 1.0}),
        ("learning_rate=0.2", "turquoise", {"learning_rate": 0.2, "subsample": 1.0}),
        ("subsample=0.5", "blue", {"learning_rate": 1.0, "subsample": 0.5}),
        (
            "learning_rate=0.2, subsample=0.5",
            "gray",
            {"learning_rate": 0.2, "subsample": 0.5},
        ),
        (
            "learning_rate=0.2, max_features=2",
            "magenta",
            {"learning_rate": 0.2, "max_features": 2},
        ),
    ]:
        params = dict(original_params)
        params.update(setting)

        clf = ensemble.GradientBoostingClassifier(**params)
        clf.fit(X_train, y_train)

        # compute test set deviance
        test_deviance = np.zeros((params["n_estimators"],), dtype=np.float64)

        for i, y_proba in enumerate(clf.staged_predict_proba(X_test)):
            test_deviance[i] = 2 * log_loss(y_test, y_proba[:, 1])

        plt.plot(
            (np.arange(test_deviance.shape[0]) + 1)[::5],
            test_deviance[::5],
            "-",
            color=color,
            label=label,
        )

    plt.legend(loc="upper right")
    plt.xlabel("Boosting Iterations")
    plt.ylabel("Test Set Deviance")

    plt.show()


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

   **Total running time of the script:** (0 minutes 8.286 seconds)


.. _sphx_glr_download_auto_examples_ensemble_plot_gradient_boosting_regularization.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/1.6.X?urlpath=lab/tree/notebooks/auto_examples/ensemble/plot_gradient_boosting_regularization.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

      .. image:: images/jupyterlite_badge_logo.svg
        :target: ../../lite/lab/index.html?path=auto_examples/ensemble/plot_gradient_boosting_regularization.ipynb
        :alt: Launch JupyterLite
        :width: 150 px

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

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

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

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_gradient_boosting_regularization.zip <plot_gradient_boosting_regularization.zip>`


.. include:: plot_gradient_boosting_regularization.recommendations


.. only:: html

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

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