
.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/classification/plot_digits_classification.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

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

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

.. _sphx_glr_auto_examples_classification_plot_digits_classification.py:


================================
Recognizing hand-written digits
================================

An example showing how the scikit-learn can be used to recognize images of
hand-written digits.

This example is commented in the
:ref:`tutorial section of the user manual <introduction>`.

.. GENERATED FROM PYTHON SOURCE LINES 13-72



.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /auto_examples/classification/images/sphx_glr_plot_digits_classification_001.png
         :alt: Training: 0, Training: 1, Training: 2, Training: 3, Prediction: 8, Prediction: 8, Prediction: 4, Prediction: 9
         :srcset: /auto_examples/classification/images/sphx_glr_plot_digits_classification_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/classification/images/sphx_glr_plot_digits_classification_002.png
         :alt: Confusion Matrix
         :srcset: /auto_examples/classification/images/sphx_glr_plot_digits_classification_002.png
         :class: sphx-glr-multi-img


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

 Out:

 .. code-block:: none


    Classification report for classifier SVC(gamma=0.001):
                  precision    recall  f1-score   support

               0       1.00      0.99      0.99        88
               1       0.99      0.97      0.98        91
               2       0.99      0.99      0.99        86
               3       0.98      0.87      0.92        91
               4       0.99      0.96      0.97        92
               5       0.95      0.97      0.96        91
               6       0.99      0.99      0.99        91
               7       0.96      0.99      0.97        89
               8       0.94      1.00      0.97        88
               9       0.93      0.98      0.95        92

        accuracy                           0.97       899
       macro avg       0.97      0.97      0.97       899
    weighted avg       0.97      0.97      0.97       899


    Confusion matrix:
    [[87  0  0  0  1  0  0  0  0  0]
     [ 0 88  1  0  0  0  0  0  1  1]
     [ 0  0 85  1  0  0  0  0  0  0]
     [ 0  0  0 79  0  3  0  4  5  0]
     [ 0  0  0  0 88  0  0  0  0  4]
     [ 0  0  0  0  0 88  1  0  0  2]
     [ 0  1  0  0  0  0 90  0  0  0]
     [ 0  0  0  0  0  1  0 88  0  0]
     [ 0  0  0  0  0  0  0  0 88  0]
     [ 0  0  0  1  0  1  0  0  0 90]]






|

.. code-block:: default

    print(__doc__)

    # Author: Gael Varoquaux <gael dot varoquaux at normalesup dot org>
    # License: BSD 3 clause

    # Standard scientific Python imports
    import matplotlib.pyplot as plt

    # Import datasets, classifiers and performance metrics
    from sklearn import datasets, svm, metrics
    from sklearn.model_selection import train_test_split

    # The digits dataset
    digits = datasets.load_digits()

    # The data that we are interested in is made of 8x8 images of digits, let's
    # have a look at the first 4 images, stored in the `images` attribute of the
    # dataset.  If we were working from image files, we could load them using
    # matplotlib.pyplot.imread.  Note that each image must have the same size. For these
    # images, we know which digit they represent: it is given in the 'target' of
    # the dataset.
    _, axes = plt.subplots(2, 4)
    images_and_labels = list(zip(digits.images, digits.target))
    for ax, (image, label) in zip(axes[0, :], images_and_labels[:4]):
        ax.set_axis_off()
        ax.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
        ax.set_title('Training: %i' % label)

    # To apply a classifier on this data, we need to flatten the image, to
    # turn the data in a (samples, feature) matrix:
    n_samples = len(digits.images)
    data = digits.images.reshape((n_samples, -1))

    # Create a classifier: a support vector classifier
    classifier = svm.SVC(gamma=0.001)

    # Split data into train and test subsets
    X_train, X_test, y_train, y_test = train_test_split(
        data, digits.target, test_size=0.5, shuffle=False)

    # We learn the digits on the first half of the digits
    classifier.fit(X_train, y_train)

    # Now predict the value of the digit on the second half:
    predicted = classifier.predict(X_test)

    images_and_predictions = list(zip(digits.images[n_samples // 2:], predicted))
    for ax, (image, prediction) in zip(axes[1, :], images_and_predictions[:4]):
        ax.set_axis_off()
        ax.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
        ax.set_title('Prediction: %i' % prediction)

    print("Classification report for classifier %s:\n%s\n"
          % (classifier, metrics.classification_report(y_test, predicted)))
    disp = metrics.plot_confusion_matrix(classifier, X_test, y_test)
    disp.figure_.suptitle("Confusion Matrix")
    print("Confusion matrix:\n%s" % disp.confusion_matrix)

    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.376 seconds)


.. _sphx_glr_download_auto_examples_classification_plot_digits_classification.py:


.. only :: html

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



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

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



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

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


.. only:: html

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

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