.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/classification/plot_classification_probability.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_classification_plot_classification_probability.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_classification_plot_classification_probability.py:


===============================
رسم احتمالية التصنيف
===============================

ارسم احتمالية التصنيف لمصنفات مختلفة. نستخدم مجموعة بيانات من 3 فئات، ونصنفها باستخدام مصنف الدعم الموجه، والانحدار اللوجستي المعاقب L1 وL2 (متعدد الحدود متعدد الفئات)، وإصدار One-Vs-Rest مع الانحدار اللوجستي، وتصنيف عملية جاوس.

SVC الخطي ليس مصنف احتمالي بشكل افتراضي، ولكنه يحتوي على خيار معايرة مدمج مُمكّن في هذا المثال (`probability=True`).

الانحدار اللوجستي مع One-Vs-Rest ليس مصنف متعدد الفئات بشكل افتراضي. ونتيجة لذلك، يواجه صعوبة أكبر في فصل الفئة 2 و3 عن المصنفات الأخرى.

.. GENERATED FROM PYTHON SOURCE LINES 12-87



.. image-sg:: /auto_examples/classification/images/sphx_glr_plot_classification_probability_001.png
   :alt: Class 0, Class 1, Class 2, Class 0, Class 1, Class 2, Class 0, Class 1, Class 2, Class 0, Class 1, Class 2, Class 0, Class 1, Class 2, Probability
   :srcset: /auto_examples/classification/images/sphx_glr_plot_classification_probability_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Accuracy (train) for L1 logistic: 83.3%
    Accuracy (train) for L2 logistic (Multinomial): 82.7%
    Accuracy (train) for L2 logistic (OvR): 79.3%
    Accuracy (train) for Linear SVC: 82.0%
    Accuracy (train) for GPC: 82.7%






|

.. code-block:: Python

    # المؤلفون: مطوري سكايت-ليرن
    # معرف-ترخيص-SPDX: BSD-3-Clause

    import matplotlib.pyplot as plt
    import numpy as np
    from matplotlib import cm

    from sklearn import datasets
    from sklearn.gaussian_process import GaussianProcessClassifier
    from sklearn.gaussian_process.kernels import RBF
    from sklearn.inspection import DecisionBoundaryDisplay
    from sklearn.linear_model import LogisticRegression
    from sklearn.metrics import accuracy_score
    from sklearn.multiclass import OneVsRestClassifier
    from sklearn.svm import SVC

    iris = datasets.load_iris()
    X = iris.data[:, 0:2]  # نأخذ فقط الخاصيتين الأوليين للتصور
    y = iris.target

    n_features = X.shape[1]

    C = 10
    kernel = 1.0 * RBF([1.0, 1.0])  # لتصنيف GPC

    # إنشاء مصنفات مختلفة.
    classifiers = {
        "L1 logistic": LogisticRegression(C=C, penalty="l1", solver="saga", max_iter=10000),
        "L2 logistic (Multinomial)": LogisticRegression(
            C=C, penalty="l2", solver="saga", max_iter=10000
        ),
        "L2 logistic (OvR)": OneVsRestClassifier(
            LogisticRegression(C=C, penalty="l2", solver="saga", max_iter=10000)
        ),
        "Linear SVC": SVC(kernel="linear", C=C, probability=True, random_state=0),
        "GPC": GaussianProcessClassifier(kernel),
    }

    n_classifiers = len(classifiers)

    fig, axes = plt.subplots(
        nrows=n_classifiers,
        ncols=len(iris.target_names),
        figsize=(3 * 2, n_classifiers * 2),
    )
    for classifier_idx, (name, classifier) in enumerate(classifiers.items()):
        y_pred = classifier.fit(X, y).predict(X)
        accuracy = accuracy_score(y, y_pred)
        print(f"Accuracy (train) for {name}: {accuracy:0.1%}")
        for label in np.unique(y):
            # ارسم تقدير الاحتمالية المقدم من المصنف
            disp = DecisionBoundaryDisplay.from_estimator(
                classifier,
                X,
                response_method="predict_proba",
                class_of_interest=label,
                ax=axes[classifier_idx, label],
                vmin=0,
                vmax=1,
            )
            axes[classifier_idx, label].set_title(f"Class {label}")
            # ارسم البيانات المتوقعة للانتماء إلى الفئة المعطاة
            mask_y_pred = y_pred == label
            axes[classifier_idx, label].scatter(
                X[mask_y_pred, 0], X[mask_y_pred, 1], marker="o", c="w", edgecolor="k"
            )
            axes[classifier_idx, label].set(xticks=(), yticks=())
        axes[classifier_idx, 0].set_ylabel(name)

    ax = plt.axes([0.15, 0.04, 0.7, 0.02])
    plt.title("Probability")
    _ = plt.colorbar(
        cm.ScalarMappable(norm=None, cmap="viridis"), cax=ax, orientation="horizontal"
    )

    plt.show()

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

   **Total running time of the script:** (0 minutes 2.332 seconds)


.. _sphx_glr_download_auto_examples_classification_plot_classification_probability.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/classification/plot_classification_probability.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

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

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

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

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

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

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

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


.. include:: plot_classification_probability.recommendations


.. only:: html

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

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