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


=============================
递归特征消除
=============================

本示例演示了如何使用递归特征消除（:class:`~sklearn.feature_selection.RFE` ）来确定单个像素在手写数字分类中的重要性。:class:`~sklearn.feature_selection.RFE` 递归地移除最不重要的特征，根据其重要性分配排名，其中较高的 `ranking_` 值表示较低的重要性。排名通过蓝色阴影和像素注释进行可视化，以提高清晰度。正如预期的那样，位于图像中心的像素比边缘的像素更具预测性。

.. note::

    另请参见 :ref:`sphx_glr_auto_examples_feature_selection_plot_rfe_with_cross_validation.py` 

.. GENERATED FROM PYTHON SOURCE LINES 13-48



.. image-sg:: /auto_examples/feature_selection/images/sphx_glr_plot_rfe_digits_001.png
   :alt: Ranking of pixels with RFE (Logistic Regression)
   :srcset: /auto_examples/feature_selection/images/sphx_glr_plot_rfe_digits_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import matplotlib.pyplot as plt

    from sklearn.datasets import load_digits
    from sklearn.feature_selection import RFE
    from sklearn.linear_model import LogisticRegression
    from sklearn.pipeline import Pipeline
    from sklearn.preprocessing import MinMaxScaler

    # 加载数字数据集
    digits = load_digits()
    X = digits.images.reshape((len(digits.images), -1))
    y = digits.target

    pipe = Pipeline(
        [
            ("scaler", MinMaxScaler()),
            ("rfe", RFE(estimator=LogisticRegression(), n_features_to_select=1, step=1)),
        ]
    )

    pipe.fit(X, y)
    ranking = pipe.named_steps["rfe"].ranking_.reshape(digits.images[0].shape)

    # 绘制像素排名
    plt.matshow(ranking, cmap=plt.cm.Blues)

    # 添加像素编号的注释
    for i in range(ranking.shape[0]):
        for j in range(ranking.shape[1]):
            plt.text(j, i, str(ranking[i, j]), ha="center", va="center", color="black")

    plt.colorbar()
    plt.title("Ranking of pixels with RFE\n(Logistic Regression)")
    plt.show()


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

   **Total running time of the script:** (0 minutes 2.426 seconds)


.. _sphx_glr_download_auto_examples_feature_selection_plot_rfe_digits.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/feature_selection/plot_rfe_digits.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_rfe_digits.recommendations


.. only:: html

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

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