.. note::
    :class: sphx-glr-download-link-note

    Click :ref:`here <sphx_glr_download_auto_examples_exercises_plot_digits_classification_exercise.py>` to download the full example code
.. 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`.





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

 Out:

 .. code-block:: none

    KNN score: 0.961111
    LogisticRegression score: 0.933333




|


.. code-block:: python

    print(__doc__)

    from sklearn import datasets, neighbors, linear_model

    digits = datasets.load_digits()
    X_digits = digits.data / digits.data.max()
    y_digits = digits.target

    n_samples = len(X_digits)

    X_train = X_digits[:int(.9 * n_samples)]
    y_train = y_digits[:int(.9 * n_samples)]
    X_test = X_digits[int(.9 * n_samples):]
    y_test = y_digits[int(.9 * n_samples):]

    knn = neighbors.KNeighborsClassifier()
    logistic = linear_model.LogisticRegression(solver='lbfgs', max_iter=1000,
                                               multi_class='multinomial')

    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))

**Total running time of the script:** ( 0 minutes  0.432 seconds)


.. _sphx_glr_download_auto_examples_exercises_plot_digits_classification_exercise.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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