.. 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_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 `. .. rst-class:: sphx-glr-horizontal * .. image:: /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 :class: sphx-glr-multi-img * .. image:: /auto_examples/classification/images/sphx_glr_plot_digits_classification_002.png :alt: Confusion Matrix :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 # 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 0.471 seconds) .. _sphx_glr_download_auto_examples_classification_plot_digits_classification.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.23.X?urlpath=lab/tree/notebooks/auto_examples/classification/plot_digits_classification.ipynb :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_digits_classification.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_digits_classification.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_