35 lines
954 B
Python
35 lines
954 B
Python
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"""
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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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from sklearn import datasets, linear_model, neighbors
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X_digits, y_digits = datasets.load_digits(return_X_y=True)
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X_digits = X_digits / X_digits.max()
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n_samples = len(X_digits)
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X_train = X_digits[: int(0.9 * n_samples)]
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y_train = y_digits[: int(0.9 * n_samples)]
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X_test = X_digits[int(0.9 * n_samples) :]
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y_test = y_digits[int(0.9 * n_samples) :]
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knn = neighbors.KNeighborsClassifier()
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logistic = linear_model.LogisticRegression(max_iter=1000)
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print("KNN score: %f" % knn.fit(X_train, y_train).score(X_test, y_test))
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print(
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"LogisticRegression score: %f"
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% logistic.fit(X_train, y_train).score(X_test, y_test)
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)
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