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

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

.. _sphx_glr_auto_examples_model_selection_plot_roc_crossval.py:


=============================================================
接收者操作特性（ROC）与交叉验证
=============================================================

本示例展示了如何使用交叉验证来估计和可视化接收者操作特性（ROC）指标的方差。

ROC 曲线通常在 Y 轴上显示真正率（TPR），在 X 轴上显示假正率（FPR）。这意味着图的左上角是“理想”点——FPR 为零，TPR 为一。这虽然不太现实，但通常来说，曲线下面积（AUC）越大越好。ROC 曲线的“陡峭度”也很重要，因为理想情况下应最大化 TPR，同时最小化 FPR。

本示例展示了通过 K 折交叉验证创建的不同数据集的 ROC 响应。通过这些曲线，可以计算平均 AUC，并观察当训练集被分成不同子集时曲线的方差。这大致显示了分类器输出如何受训练数据变化的影响，以及 K 折交叉验证生成的不同分割之间的差异。

.. NOTE::

    请参阅 :ref:`sphx_glr_auto_examples_model_selection_plot_roc.py` ，该示例补充了当前示例，解释了将指标推广到多类分类器的平均策略。

.. GENERATED FROM PYTHON SOURCE LINES 18-24

加载和准备数据
=====================

我们导入了 :ref:`iris_dataset` ，其中包含3个类别，每个类别对应一种鸢尾花类型。一个类别与其他两个类别是线性可分的；而后两者之间 **不是** 线性可分的。

在接下来的步骤中，我们通过去除“virginica”类（ `class_id=2` ）来将数据集二值化。这意味着“versicolor”类（ `class_id=1` ）被视为正类，而“setosa”类（ `class_id=0` ）被视为负类。

.. GENERATED FROM PYTHON SOURCE LINES 24-35

.. code-block:: Python


    import numpy as np

    from sklearn.datasets import load_iris

    iris = load_iris()
    target_names = iris.target_names
    X, y = iris.data, iris.target
    X, y = X[y != 2], y[y != 2]
    n_samples, n_features = X.shape








.. GENERATED FROM PYTHON SOURCE LINES 36-37

我们还添加了噪声特征以增加问题的难度。

.. GENERATED FROM PYTHON SOURCE LINES 37-41

.. code-block:: Python


    random_state = np.random.RandomState(0)
    X = np.concatenate([X, random_state.randn(n_samples, 200 * n_features)], axis=1)








.. GENERATED FROM PYTHON SOURCE LINES 42-46

分类和ROC分析
-------------------------------

我们在这里运行一个 :class:`~sklearn.svm.SVC` 分类器，并使用交叉验证绘制每折的 ROC 曲线。请注意，用于定义机会水平（虚线 ROC 曲线）的基线是一个总是预测最频繁类别的分类器。

.. GENERATED FROM PYTHON SOURCE LINES 46-111

.. code-block:: Python


    import matplotlib.pyplot as plt

    from sklearn import svm
    from sklearn.metrics import RocCurveDisplay, auc
    from sklearn.model_selection import StratifiedKFold

    n_splits = 6
    cv = StratifiedKFold(n_splits=n_splits)
    classifier = svm.SVC(kernel="linear", probability=True, random_state=random_state)

    tprs = []
    aucs = []
    mean_fpr = np.linspace(0, 1, 100)

    fig, ax = plt.subplots(figsize=(6, 6))
    for fold, (train, test) in enumerate(cv.split(X, y)):
        classifier.fit(X[train], y[train])
        viz = RocCurveDisplay.from_estimator(
            classifier,
            X[test],
            y[test],
            name=f"ROC fold {fold}",
            alpha=0.3,
            lw=1,
            ax=ax,
            plot_chance_level=(fold == n_splits - 1),
        )
        interp_tpr = np.interp(mean_fpr, viz.fpr, viz.tpr)
        interp_tpr[0] = 0.0
        tprs.append(interp_tpr)
        aucs.append(viz.roc_auc)

    mean_tpr = np.mean(tprs, axis=0)
    mean_tpr[-1] = 1.0
    mean_auc = auc(mean_fpr, mean_tpr)
    std_auc = np.std(aucs)
    ax.plot(
        mean_fpr,
        mean_tpr,
        color="b",
        label=r"Mean ROC (AUC = %0.2f $\pm$ %0.2f)" % (mean_auc, std_auc),
        lw=2,
        alpha=0.8,
    )

    std_tpr = np.std(tprs, axis=0)
    tprs_upper = np.minimum(mean_tpr + std_tpr, 1)
    tprs_lower = np.maximum(mean_tpr - std_tpr, 0)
    ax.fill_between(
        mean_fpr,
        tprs_lower,
        tprs_upper,
        color="grey",
        alpha=0.2,
        label=r"$\pm$ 1 std. dev.",
    )

    ax.set(
        xlabel="False Positive Rate",
        ylabel="True Positive Rate",
        title=f"Mean ROC curve with variability\n(Positive label '{target_names[1]}')",
    )
    ax.legend(loc="lower right")
    plt.show()



.. image-sg:: /auto_examples/model_selection/images/sphx_glr_plot_roc_crossval_001.png
   :alt: Mean ROC curve with variability (Positive label 'versicolor')
   :srcset: /auto_examples/model_selection/images/sphx_glr_plot_roc_crossval_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.105 seconds)


.. _sphx_glr_download_auto_examples_model_selection_plot_roc_crossval.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/model_selection/plot_roc_crossval.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_roc_crossval.recommendations


.. only:: html

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

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