.. 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 JupyterLite or Binder

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

.. _sphx_glr_auto_examples_feature_selection_plot_rfe_digits.py:


=============================
Recursive feature elimination
=============================

This example demonstrates how Recursive Feature Elimination
(:class:`~sklearn.feature_selection.RFE`) can be used to determine the
importance of individual pixels for classifying handwritten digits.
:class:`~sklearn.feature_selection.RFE` recursively removes the least
significant features, assigning ranks based on their importance, where higher
`ranking_` values denote lower importance. The ranking is visualized using both
shades of blue and pixel annotations for clarity. As expected, pixels positioned
at the center of the image tend to be more predictive than those near the edges.

.. note::

    See also :ref:`sphx_glr_auto_examples_feature_selection_plot_rfe_with_cross_validation.py`

.. GENERATED FROM PYTHON SOURCE LINES 20-55



.. 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

    # Load the digits dataset
    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)

    # Plot pixel ranking
    plt.matshow(ranking, cmap=plt.cm.Blues)

    # Add annotations for pixel numbers
    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 2.952 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:: lite-badge

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        :target: ../../lite/lab/?path=auto_examples/feature_selection/plot_rfe_digits.ipynb
        :alt: Launch JupyterLite
        :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>`


.. include:: plot_rfe_digits.recommendations


.. only:: html

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

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