35 lines
1.0 KiB
Python
35 lines
1.0 KiB
Python
"""
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================================
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Digits Classification Exercise
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================================
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A tutorial exercise regarding the use of classification techniques on
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the Digits dataset.
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This exercise is used in the :ref:`clf_tut` part of the
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:ref:`supervised_learning_tut` section of the
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:ref:`stat_learn_tut_index`.
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"""
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print(__doc__)
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from sklearn import datasets, neighbors, linear_model
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digits = datasets.load_digits()
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X_digits = digits.data / digits.data.max()
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y_digits = digits.target
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n_samples = len(X_digits)
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X_train = X_digits[:int(.9 * n_samples)]
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y_train = y_digits[:int(.9 * n_samples)]
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X_test = X_digits[int(.9 * n_samples):]
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y_test = y_digits[int(.9 * n_samples):]
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knn = neighbors.KNeighborsClassifier()
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logistic = linear_model.LogisticRegression(max_iter=1000,
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multi_class='multinomial')
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print('KNN score: %f' % knn.fit(X_train, y_train).score(X_test, y_test))
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print('LogisticRegression score: %f'
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% logistic.fit(X_train, y_train).score(X_test, y_test))
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