.. 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 Click :ref:`here ` 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_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 13-33 .. rst-class:: sphx-glr-script-out Out: .. code-block:: none KNN score: 0.961111 LogisticRegression score: 0.933333 | .. code-block:: default print(__doc__) from sklearn import datasets, neighbors, linear_model 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(.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(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 0.303 seconds) .. _sphx_glr_download_auto_examples_exercises_plot_digits_classification_exercise.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.24.X?urlpath=lab/tree/notebooks/auto_examples/exercises/plot_digits_classification_exercise.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_digits_classification_exercise.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_digits_classification_exercise.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_