Note
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A tutorial exercise regarding the use of classification techniques on the Digits dataset.
This exercise is used in the Classification part of the Supervised learning: predicting an output variable from high-dimensional observations section of the A tutorial on statistical-learning for scientific data processing.
Out:
KNN score: 0.961111 LogisticRegression score: 0.933333
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 0.848 seconds)
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http://scikit-learn.org/stable/auto_examples/exercises/plot_digits_classification_exercise.html