.. 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:


================================
تنظيم التعزيز المتدرج
================================

توضيح لتأثير استراتيجيات تنظيم مختلفة
للتعزيز المتدرج. المثال مأخوذ من Hastie et al 2009 [1]_.

دالة الخسارة المستخدمة هي انحراف ذو الحدين. التنظيم عبر
الانكماش (``learning_rate < 1.0``) يحسن الأداء بشكل كبير.
بالتزامن مع الانكماش، يمكن أن ينتج التعزيز المتدرج العشوائي
(``subsample < 1.0``) نماذج أكثر دقة عن طريق تقليل
التباين عبر التجميع.
عادةً ما يؤدي الاختزال الفرعي بدون انكماش إلى أداء ضعيف.
هناك إستراتيجية أخرى لتقليل التباين وهي عن طريق الاختزال الفرعي للميزات
على غرار التقسيمات العشوائية في الغابات العشوائية
(عبر معلمة ``max_features``).

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

.. GENERATED FROM PYTHON SOURCE LINES 23-93



.. 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)

    # تعيين التصنيفات من {-1, 1} إلى {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 [
        ("بدون انكماش", "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)

        # حساب انحراف مجموعة الاختبار
        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("تكرارات التعزيز")
    plt.ylabel("انحراف مجموعة الاختبار")

    plt.show()




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

   **Total running time of the script:** (0 minutes 8.667 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/main?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>`_