.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/release_highlights/plot_release_highlights_1_2_0.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_release_highlights_plot_release_highlights_1_2_0.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_release_highlights_plot_release_highlights_1_2_0.py:


=======================================
أبرز ميزات الإصدار 1.2 من scikit-learn
=======================================

.. currentmodule:: sklearn

يسعدنا الإعلان عن إصدار scikit-learn 1.2! تم إجراء العديد من الإصلاحات والتحسينات، بالإضافة إلى بعض الميزات الرئيسية الجديدة. نستعرض أدناه بعض الميزات الرئيسية لهذا الإصدار. **للاطلاع على قائمة شاملة بجميع التغييرات**، يرجى الرجوع إلى :ref:`ملاحظات الإصدار <release_notes_1_2>`.

لتثبيت أحدث إصدار (باستخدام pip)::

    pip install --upgrade scikit-learn

أو باستخدام conda::

    conda install -c conda-forge scikit-learn

.. GENERATED FROM PYTHON SOURCE LINES 22-29

إخراج Pandas مع واجهة برمجة التطبيقات set_output
-----------------------------------
تدعم محولات scikit-learn الآن إخراج Pandas مع واجهة برمجة التطبيقات set_output.
لمزيد من المعلومات حول واجهة برمجة التطبيقات set_output، راجع المثال:
:ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py` و
# هذا الفيديو، إخراج DataFrame لـ Pandas لمحولات scikit-learn
(بعض الأمثلة) <https://youtu.be/5bCg8VfX2x8>`__.

.. GENERATED FROM PYTHON SOURCE LINES 29-50

.. code-block:: Python


    import numpy as np
    from sklearn.datasets import load_iris
    from sklearn.preprocessing import StandardScaler, KBinsDiscretizer
    from sklearn.compose import ColumnTransformer

    X, y = load_iris(as_frame=True, return_X_y=True)
    sepal_cols = ["sepal length (cm)", "sepal width (cm)"]
    petal_cols = ["petal length (cm)", "petal width (cm)"]

    preprocessor = ColumnTransformer(
        [
            ("scaler", StandardScaler(), sepal_cols),
            ("kbin", KBinsDiscretizer(encode="ordinal"), petal_cols),
        ],
        verbose_feature_names_out=False,
    ).set_output(transform="pandas")

    X_out = preprocessor.fit_transform(X)
    X_out.sample(n=5, random_state=0)






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>sepal length (cm)</th>
          <th>sepal width (cm)</th>
          <th>petal length (cm)</th>
          <th>petal width (cm)</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>114</th>
          <td>-0.052506</td>
          <td>-0.592373</td>
          <td>3.0</td>
          <td>4.0</td>
        </tr>
        <tr>
          <th>62</th>
          <td>0.189830</td>
          <td>-1.973554</td>
          <td>2.0</td>
          <td>1.0</td>
        </tr>
        <tr>
          <th>33</th>
          <td>-0.416010</td>
          <td>2.630382</td>
          <td>0.0</td>
          <td>1.0</td>
        </tr>
        <tr>
          <th>107</th>
          <td>1.765012</td>
          <td>-0.362176</td>
          <td>4.0</td>
          <td>3.0</td>
        </tr>
        <tr>
          <th>7</th>
          <td>-1.021849</td>
          <td>0.788808</td>
          <td>1.0</td>
          <td>1.0</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 51-57

قيود التفاعل في أشجار التدرج التدريجي القائمة على التوزيع التكراري
------------------------------------------------------------------
:class:`~ensemble.HistGradientBoostingRegressor` و
:class:`~ensemble.HistGradientBoostingClassifier` يدعمان الآن قيود التفاعل
مع معلمة `interaction_cst`. لمزيد من التفاصيل، راجع
:ref:`دليل المستخدم <interaction_cst_hgbt>`. في المثال التالي، لا يُسمح للميزات بالتفاعل.

.. GENERATED FROM PYTHON SOURCE LINES 57-67

.. code-block:: Python

    from sklearn.datasets import load_diabetes
    from sklearn.ensemble import HistGradientBoostingRegressor

    X, y = load_diabetes(return_X_y=True, as_frame=True)

    hist_no_interact = HistGradientBoostingRegressor(
        interaction_cst=[[i] for i in range(X.shape[1])], random_state=0
    )
    hist_no_interact.fit(X, y)






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <style>#sk-container-id-6 {
      /* Definition of color scheme common for light and dark mode */
      --sklearn-color-text: #000;
      --sklearn-color-text-muted: #666;
      --sklearn-color-line: gray;
      /* Definition of color scheme for unfitted estimators */
      --sklearn-color-unfitted-level-0: #fff5e6;
      --sklearn-color-unfitted-level-1: #f6e4d2;
      --sklearn-color-unfitted-level-2: #ffe0b3;
      --sklearn-color-unfitted-level-3: chocolate;
      /* Definition of color scheme for fitted estimators */
      --sklearn-color-fitted-level-0: #f0f8ff;
      --sklearn-color-fitted-level-1: #d4ebff;
      --sklearn-color-fitted-level-2: #b3dbfd;
      --sklearn-color-fitted-level-3: cornflowerblue;

      /* Specific color for light theme */
      --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
      --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));
      --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
      --sklearn-color-icon: #696969;

      @media (prefers-color-scheme: dark) {
        /* Redefinition of color scheme for dark theme */
        --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
        --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));
        --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
        --sklearn-color-icon: #878787;
      }
    }

    #sk-container-id-6 {
      color: var(--sklearn-color-text);
    }

    #sk-container-id-6 pre {
      padding: 0;
    }

    #sk-container-id-6 input.sk-hidden--visually {
      border: 0;
      clip: rect(1px 1px 1px 1px);
      clip: rect(1px, 1px, 1px, 1px);
      height: 1px;
      margin: -1px;
      overflow: hidden;
      padding: 0;
      position: absolute;
      width: 1px;
    }

    #sk-container-id-6 div.sk-dashed-wrapped {
      border: 1px dashed var(--sklearn-color-line);
      margin: 0 0.4em 0.5em 0.4em;
      box-sizing: border-box;
      padding-bottom: 0.4em;
      background-color: var(--sklearn-color-background);
    }

    #sk-container-id-6 div.sk-container {
      /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
         but bootstrap.min.css set `[hidden] { display: none !important; }`
         so we also need the `!important` here to be able to override the
         default hidden behavior on the sphinx rendered scikit-learn.org.
         See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
      display: inline-block !important;
      position: relative;
    }

    #sk-container-id-6 div.sk-text-repr-fallback {
      display: none;
    }

    div.sk-parallel-item,
    div.sk-serial,
    div.sk-item {
      /* draw centered vertical line to link estimators */
      background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
      background-size: 2px 100%;
      background-repeat: no-repeat;
      background-position: center center;
    }

    /* Parallel-specific style estimator block */

    #sk-container-id-6 div.sk-parallel-item::after {
      content: "";
      width: 100%;
      border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
      flex-grow: 1;
    }

    #sk-container-id-6 div.sk-parallel {
      display: flex;
      align-items: stretch;
      justify-content: center;
      background-color: var(--sklearn-color-background);
      position: relative;
    }

    #sk-container-id-6 div.sk-parallel-item {
      display: flex;
      flex-direction: column;
    }

    #sk-container-id-6 div.sk-parallel-item:first-child::after {
      align-self: flex-end;
      width: 50%;
    }

    #sk-container-id-6 div.sk-parallel-item:last-child::after {
      align-self: flex-start;
      width: 50%;
    }

    #sk-container-id-6 div.sk-parallel-item:only-child::after {
      width: 0;
    }

    /* Serial-specific style estimator block */

    #sk-container-id-6 div.sk-serial {
      display: flex;
      flex-direction: column;
      align-items: center;
      background-color: var(--sklearn-color-background);
      padding-right: 1em;
      padding-left: 1em;
    }


    /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
    clickable and can be expanded/collapsed.
    - Pipeline and ColumnTransformer use this feature and define the default style
    - Estimators will overwrite some part of the style using the `sk-estimator` class
    */

    /* Pipeline and ColumnTransformer style (default) */

    #sk-container-id-6 div.sk-toggleable {
      /* Default theme specific background. It is overwritten whether we have a
      specific estimator or a Pipeline/ColumnTransformer */
      background-color: var(--sklearn-color-background);
    }

    /* Toggleable label */
    #sk-container-id-6 label.sk-toggleable__label {
      cursor: pointer;
      display: flex;
      width: 100%;
      margin-bottom: 0;
      padding: 0.5em;
      box-sizing: border-box;
      text-align: center;
      align-items: start;
      justify-content: space-between;
      gap: 0.5em;
    }

    #sk-container-id-6 label.sk-toggleable__label .caption {
      font-size: 0.6rem;
      font-weight: lighter;
      color: var(--sklearn-color-text-muted);
    }

    #sk-container-id-6 label.sk-toggleable__label-arrow:before {
      /* Arrow on the left of the label */
      content: "▸";
      float: left;
      margin-right: 0.25em;
      color: var(--sklearn-color-icon);
    }

    #sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {
      color: var(--sklearn-color-text);
    }

    /* Toggleable content - dropdown */

    #sk-container-id-6 div.sk-toggleable__content {
      max-height: 0;
      max-width: 0;
      overflow: hidden;
      text-align: left;
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-0);
    }

    #sk-container-id-6 div.sk-toggleable__content.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
    }

    #sk-container-id-6 div.sk-toggleable__content pre {
      margin: 0.2em;
      border-radius: 0.25em;
      color: var(--sklearn-color-text);
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-0);
    }

    #sk-container-id-6 div.sk-toggleable__content.fitted pre {
      /* unfitted */
      background-color: var(--sklearn-color-fitted-level-0);
    }

    #sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {
      /* Expand drop-down */
      max-height: 200px;
      max-width: 100%;
      overflow: auto;
    }

    #sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
      content: "▾";
    }

    /* Pipeline/ColumnTransformer-specific style */

    #sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
      color: var(--sklearn-color-text);
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    #sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
      background-color: var(--sklearn-color-fitted-level-2);
    }

    /* Estimator-specific style */

    /* Colorize estimator box */
    #sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    #sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-2);
    }

    #sk-container-id-6 div.sk-label label.sk-toggleable__label,
    #sk-container-id-6 div.sk-label label {
      /* The background is the default theme color */
      color: var(--sklearn-color-text-on-default-background);
    }

    /* On hover, darken the color of the background */
    #sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {
      color: var(--sklearn-color-text);
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    /* Label box, darken color on hover, fitted */
    #sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
      color: var(--sklearn-color-text);
      background-color: var(--sklearn-color-fitted-level-2);
    }

    /* Estimator label */

    #sk-container-id-6 div.sk-label label {
      font-family: monospace;
      font-weight: bold;
      display: inline-block;
      line-height: 1.2em;
    }

    #sk-container-id-6 div.sk-label-container {
      text-align: center;
    }

    /* Estimator-specific */
    #sk-container-id-6 div.sk-estimator {
      font-family: monospace;
      border: 1px dotted var(--sklearn-color-border-box);
      border-radius: 0.25em;
      box-sizing: border-box;
      margin-bottom: 0.5em;
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-0);
    }

    #sk-container-id-6 div.sk-estimator.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
    }

    /* on hover */
    #sk-container-id-6 div.sk-estimator:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    #sk-container-id-6 div.sk-estimator.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-2);
    }

    /* Specification for estimator info (e.g. "i" and "?") */

    /* Common style for "i" and "?" */

    .sk-estimator-doc-link,
    a:link.sk-estimator-doc-link,
    a:visited.sk-estimator-doc-link {
      float: right;
      font-size: smaller;
      line-height: 1em;
      font-family: monospace;
      background-color: var(--sklearn-color-background);
      border-radius: 1em;
      height: 1em;
      width: 1em;
      text-decoration: none !important;
      margin-left: 0.5em;
      text-align: center;
      /* unfitted */
      border: var(--sklearn-color-unfitted-level-1) 1pt solid;
      color: var(--sklearn-color-unfitted-level-1);
    }

    .sk-estimator-doc-link.fitted,
    a:link.sk-estimator-doc-link.fitted,
    a:visited.sk-estimator-doc-link.fitted {
      /* fitted */
      border: var(--sklearn-color-fitted-level-1) 1pt solid;
      color: var(--sklearn-color-fitted-level-1);
    }

    /* On hover */
    div.sk-estimator:hover .sk-estimator-doc-link:hover,
    .sk-estimator-doc-link:hover,
    div.sk-label-container:hover .sk-estimator-doc-link:hover,
    .sk-estimator-doc-link:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-3);
      color: var(--sklearn-color-background);
      text-decoration: none;
    }

    div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
    .sk-estimator-doc-link.fitted:hover,
    div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
    .sk-estimator-doc-link.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-3);
      color: var(--sklearn-color-background);
      text-decoration: none;
    }

    /* Span, style for the box shown on hovering the info icon */
    .sk-estimator-doc-link span {
      display: none;
      z-index: 9999;
      position: relative;
      font-weight: normal;
      right: .2ex;
      padding: .5ex;
      margin: .5ex;
      width: min-content;
      min-width: 20ex;
      max-width: 50ex;
      color: var(--sklearn-color-text);
      box-shadow: 2pt 2pt 4pt #999;
      /* unfitted */
      background: var(--sklearn-color-unfitted-level-0);
      border: .5pt solid var(--sklearn-color-unfitted-level-3);
    }

    .sk-estimator-doc-link.fitted span {
      /* fitted */
      background: var(--sklearn-color-fitted-level-0);
      border: var(--sklearn-color-fitted-level-3);
    }

    .sk-estimator-doc-link:hover span {
      display: block;
    }

    /* "?"-specific style due to the `<a>` HTML tag */

    #sk-container-id-6 a.estimator_doc_link {
      float: right;
      font-size: 1rem;
      line-height: 1em;
      font-family: monospace;
      background-color: var(--sklearn-color-background);
      border-radius: 1rem;
      height: 1rem;
      width: 1rem;
      text-decoration: none;
      /* unfitted */
      color: var(--sklearn-color-unfitted-level-1);
      border: var(--sklearn-color-unfitted-level-1) 1pt solid;
    }

    #sk-container-id-6 a.estimator_doc_link.fitted {
      /* fitted */
      border: var(--sklearn-color-fitted-level-1) 1pt solid;
      color: var(--sklearn-color-fitted-level-1);
    }

    /* On hover */
    #sk-container-id-6 a.estimator_doc_link:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-3);
      color: var(--sklearn-color-background);
      text-decoration: none;
    }

    #sk-container-id-6 a.estimator_doc_link.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-3);
    }
    </style><div id="sk-container-id-6" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>HistGradientBoostingRegressor(interaction_cst=[[0], [1], [2], [3], [4], [5],
                                                   [6], [7], [8], [9]],
                                  random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-6" type="checkbox" checked><label for="sk-estimator-id-6" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>HistGradientBoostingRegressor</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html">?<span>Documentation for HistGradientBoostingRegressor</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted"><pre>HistGradientBoostingRegressor(interaction_cst=[[0], [1], [2], [3], [4], [5],
                                                   [6], [7], [8], [9]],
                                  random_state=0)</pre></div> </div></div></div></div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 68-72

شاشات العرض الجديدة والمحسنة
-------------------------
:class:`~metrics.PredictionErrorDisplay` توفر طريقة لتحليل نماذج الانحدار
بطريقة نوعية.

.. GENERATED FROM PYTHON SOURCE LINES 72-83

.. code-block:: Python

    import matplotlib.pyplot as plt
    from sklearn.metrics import PredictionErrorDisplay

    fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))
    _ = PredictionErrorDisplay.from_estimator(
        hist_no_interact, X, y, kind="actual_vs_predicted", ax=axs[0]
    )
    _ = PredictionErrorDisplay.from_estimator(
        hist_no_interact, X, y, kind="residual_vs_predicted", ax=axs[1]
    )




.. image-sg:: /auto_examples/release_highlights/images/sphx_glr_plot_release_highlights_1_2_0_001.png
   :alt: plot release highlights 1 2 0
   :srcset: /auto_examples/release_highlights/images/sphx_glr_plot_release_highlights_1_2_0_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 84-86

:class:`~model_selection.LearningCurveDisplay` متاح الآن لعرض
النتائج من :func:`~model_selection.learning_curve`.

.. GENERATED FROM PYTHON SOURCE LINES 86-92

.. code-block:: Python

    from sklearn.model_selection import LearningCurveDisplay

    _ = LearningCurveDisplay.from_estimator(
        hist_no_interact, X, y, cv=5, n_jobs=2, train_sizes=np.linspace(0.1, 1, 5)
    )




.. image-sg:: /auto_examples/release_highlights/images/sphx_glr_plot_release_highlights_1_2_0_002.png
   :alt: plot release highlights 1 2 0
   :srcset: /auto_examples/release_highlights/images/sphx_glr_plot_release_highlights_1_2_0_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 93-96

:class:`~inspection.PartialDependenceDisplay` تعرض معلمة جديدة
`categorical_features` لعرض الاعتماد الجزئي للميزات التصنيفية
باستخدام مخططات الأعمدة والخرائط الحرارية.

.. GENERATED FROM PYTHON SOURCE LINES 96-103

.. code-block:: Python

    from sklearn.datasets import fetch_openml

    X, y = fetch_openml(
        "titanic", version=1, as_frame=True, return_X_y=True, parser="pandas"
    )
    X = X.select_dtypes(["number", "category"]).drop(columns=["body"])








.. GENERATED FROM PYTHON SOURCE LINES 104-116

.. code-block:: Python

    from sklearn.preprocessing import OrdinalEncoder
    from sklearn.pipeline import make_pipeline

    categorical_features = ["pclass", "sex", "embarked"]
    model = make_pipeline(
        ColumnTransformer(
            transformers=[("cat", OrdinalEncoder(), categorical_features)],
            remainder="passthrough",
        ),
        HistGradientBoostingRegressor(random_state=0),
    ).fit(X, y)








.. GENERATED FROM PYTHON SOURCE LINES 117-128

.. code-block:: Python

    from sklearn.inspection import PartialDependenceDisplay

    fig, ax = plt.subplots(figsize=(14, 4), constrained_layout=True)
    _ = PartialDependenceDisplay.from_estimator(
        model,
        X,
        features=["age", "sex", ("pclass", "sex")],
        categorical_features=categorical_features,
        ax=ax,
    )




.. image-sg:: /auto_examples/release_highlights/images/sphx_glr_plot_release_highlights_1_2_0_003.png
   :alt: plot release highlights 1 2 0
   :srcset: /auto_examples/release_highlights/images/sphx_glr_plot_release_highlights_1_2_0_003.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 129-135

معالج أسرع في :func:`~datasets.fetch_openml`
-----------------------------------------------
:func:`~datasets.fetch_openml` يدعم الآن معالج جديد "pandas" وهو أكثر
كفاءة في الذاكرة والمعالجة. في الإصدار 1.4، سيتم تغيير المعالج الافتراضي إلى
`parser="auto"` والذي سيستخدم تلقائيًا معالج "pandas" للبيانات الكثيفة
و"liac-arff" للبيانات المتناثرة.

.. GENERATED FROM PYTHON SOURCE LINES 135-140

.. code-block:: Python

    X, y = fetch_openml(
        "titanic", version=1, as_frame=True, return_X_y=True, parser="pandas"
    )
    X.head()






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>pclass</th>
          <th>name</th>
          <th>sex</th>
          <th>age</th>
          <th>sibsp</th>
          <th>parch</th>
          <th>ticket</th>
          <th>fare</th>
          <th>cabin</th>
          <th>embarked</th>
          <th>boat</th>
          <th>body</th>
          <th>home.dest</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>1</td>
          <td>Allen, Miss. Elisabeth Walton</td>
          <td>female</td>
          <td>29.0000</td>
          <td>0</td>
          <td>0</td>
          <td>24160</td>
          <td>211.3375</td>
          <td>B5</td>
          <td>S</td>
          <td>2</td>
          <td>NaN</td>
          <td>St Louis, MO</td>
        </tr>
        <tr>
          <th>1</th>
          <td>1</td>
          <td>Allison, Master. Hudson Trevor</td>
          <td>male</td>
          <td>0.9167</td>
          <td>1</td>
          <td>2</td>
          <td>113781</td>
          <td>151.5500</td>
          <td>C22 C26</td>
          <td>S</td>
          <td>11</td>
          <td>NaN</td>
          <td>Montreal, PQ / Chesterville, ON</td>
        </tr>
        <tr>
          <th>2</th>
          <td>1</td>
          <td>Allison, Miss. Helen Loraine</td>
          <td>female</td>
          <td>2.0000</td>
          <td>1</td>
          <td>2</td>
          <td>113781</td>
          <td>151.5500</td>
          <td>C22 C26</td>
          <td>S</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>Montreal, PQ / Chesterville, ON</td>
        </tr>
        <tr>
          <th>3</th>
          <td>1</td>
          <td>Allison, Mr. Hudson Joshua Creighton</td>
          <td>male</td>
          <td>30.0000</td>
          <td>1</td>
          <td>2</td>
          <td>113781</td>
          <td>151.5500</td>
          <td>C22 C26</td>
          <td>S</td>
          <td>NaN</td>
          <td>135.0</td>
          <td>Montreal, PQ / Chesterville, ON</td>
        </tr>
        <tr>
          <th>4</th>
          <td>1</td>
          <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>
          <td>female</td>
          <td>25.0000</td>
          <td>1</td>
          <td>2</td>
          <td>113781</td>
          <td>151.5500</td>
          <td>C22 C26</td>
          <td>S</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>Montreal, PQ / Chesterville, ON</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 141-147

دعم تجريبي لواجهة برمجة التطبيقات Array في :class:`~discriminant_analysis.LinearDiscriminantAnalysis`
--------------------------------------------------------------------------------------------
تمت إضافة دعم تجريبي لمواصفات `Array API <https://data-apis.org/array-api/latest/>`_
إلى :class:`~discriminant_analysis.LinearDiscriminantAnalysis`.
يمكن للمقدر الآن العمل على أي مكتبات متوافقة مع واجهة برمجة التطبيقات Array مثل
`CuPy <https://docs.cupy.dev/en/stable/overview.html>`__، وهي مكتبة صفائف معجلة بواسطة GPU. لمزيد من التفاصيل، راجع :ref:`دليل المستخدم <array_api>`.

.. GENERATED FROM PYTHON SOURCE LINES 149-161

تحسين كفاءة العديد من المقدرات
--------------------------------------
في الإصدار 1.1، تم تحسين كفاءة العديد من المقدرات التي تعتمد على حساب
المسافات الزوجية (بشكل أساسي المقدرات المتعلقة بالتجمع، وتعلم المانيفولد،
وخوارزميات البحث عن الجيران) بشكل كبير للمدخلات float64 الكثيفة.
شملت تحسينات الكفاءة بشكل خاص تقليل استهلاك الذاكرة وتحسين قابلية التوسع
على أجهزة متعددة النواة.
في الإصدار 1.2، تم تحسين كفاءة هذه المقدرات بشكل أكبر لجميع
مجموعات المدخلات الكثيفة والمبعثرة على مجموعات البيانات float32 وfloat64،
باستثناء مجموعات البيانات المبعثرة-الكثيفة والكثيفة-المبعثرة لمقاييس المسافة
Euclidean وSquared Euclidean.
يمكن العثور على قائمة مفصلة بالمقدرات المتأثرة في
:ref:`سجل التغييرات <release_notes_1_2>`.


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

   **Total running time of the script:** (0 minutes 4.811 seconds)


.. _sphx_glr_download_auto_examples_release_highlights_plot_release_highlights_1_2_0.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/release_highlights/plot_release_highlights_1_2_0.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

      .. image:: images/jupyterlite_badge_logo.svg
        :target: ../../lite/lab/index.html?path=auto_examples/release_highlights/plot_release_highlights_1_2_0.ipynb
        :alt: Launch JupyterLite
        :width: 150 px

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

      :download:`Download Jupyter notebook: plot_release_highlights_1_2_0.ipynb <plot_release_highlights_1_2_0.ipynb>`

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

      :download:`Download Python source code: plot_release_highlights_1_2_0.py <plot_release_highlights_1_2_0.py>`

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

      :download:`Download zipped: plot_release_highlights_1_2_0.zip <plot_release_highlights_1_2_0.zip>`


.. include:: plot_release_highlights_1_2_0.recommendations


.. only:: html

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

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