.. _sphx_glr_auto_examples_exercises_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`.


.. code-block:: python

    print(__doc__)

    from sklearn import datasets, neighbors, linear_model

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

    n_samples = len(X_digits)

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

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

    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 0.000 seconds)



.. container:: sphx-glr-download

    **Download Python source code:** :download:`digits_classification_exercise.py <digits_classification_exercise.py>`


.. container:: sphx-glr-download

    **Download IPython notebook:** :download:`digits_classification_exercise.ipynb <digits_classification_exercise.ipynb>`