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


================================
Digits Classification Exercise
================================

A tutorial exercise regarding the use of classification techniques on
the Digits dataset.

This exercise is used in the :ref:`clf_tut` part of the
:ref:`supervised_learning_tut` section of the
:ref:`stat_learn_tut_index`.

.. GENERATED FROM PYTHON SOURCE LINES 14-38




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

 .. code-block:: none

    KNN score: 0.961111
    LogisticRegression score: 0.933333






|

.. code-block:: Python


    # Authors: The scikit-learn developers
    # SPDX-License-Identifier: BSD-3-Clause

    from sklearn import datasets, linear_model, neighbors

    X_digits, y_digits = datasets.load_digits(return_X_y=True)
    X_digits = X_digits / X_digits.max()

    n_samples = len(X_digits)

    X_train = X_digits[: int(0.9 * n_samples)]
    y_train = y_digits[: int(0.9 * n_samples)]
    X_test = X_digits[int(0.9 * n_samples) :]
    y_test = y_digits[int(0.9 * n_samples) :]

    knn = neighbors.KNeighborsClassifier()
    logistic = linear_model.LogisticRegression(max_iter=1000)

    print("KNN score: %f" % knn.fit(X_train, y_train).score(X_test, y_test))
    print(
        "LogisticRegression score: %f"
        % logistic.fit(X_train, y_train).score(X_test, y_test)
    )


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

   **Total running time of the script:** (0 minutes 0.067 seconds)


.. _sphx_glr_download_auto_examples_exercises_plot_digits_classification_exercise.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/1.6.X?urlpath=lab/tree/notebooks/auto_examples/exercises/plot_digits_classification_exercise.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

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

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

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

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

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

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

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


.. include:: plot_digits_classification_exercise.recommendations


.. only:: html

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

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