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cross_validation.rst.txt
[1., 1.], [-1., -1.], [2., 2.]]) >>> y = np.array([0, 1, 0,...3] [0 1] [1 3] [0 2] [1 2] [0 3] [0 3] [1 2] [0 2] [1 3] [0 1]...scikit-learn.org/stable/_sources/modules/cross_validation.rst.txt -
feature_extraction.rst.txt
array([[1, 1, 1, 0, 1, 1, 1, 0], [1, 1, 0, 1, 1, 1, 0, 1]]) In...array([[0, 1, 1, 1, 0, 0, 1, 0, 1], [0, 1, 0, 1, 0, 2, 1, 0, 1], [1,...scikit-learn.org/stable/_sources/modules/feature_extraction.rst.txt -
ensemble.rst.txt
1, 2, np.nan]).reshape(-1, 1) >>> y = [0, 0, 1, 1] >>> gbdt...np.nan, 1, 2, np.nan]).reshape(-1, 1) >>> y = [0, 1, 0, 0, 1] >>>...scikit-learn.org/stable/_sources/modules/ensemble.rst.txt -
plot_release_highlights_1_7_0.rst.txt
#sk-container-id-1 pre { padding: 0; } #sk-container-id-1 input.sk-hidden--visually...ound); flex-grow: 1; } #sk-container-id-1 div.sk-parallel { display:...scikit-learn.org/stable/_sources/auto_examples/release_highlights/plot_release_highlights_1_7_0.r... -
neighbors.rst.txt
array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])...np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])...scikit-learn.org/stable/_sources/modules/neighbors.rst.txt -
plot_release_highlights_1_8_0.rst.txt
#sk-container-id-1 pre { padding: 0; } #sk-container-id-1 input.sk-hidden--visually...ound); flex-grow: 1; } #sk-container-id-1 div.sk-parallel { display:...scikit-learn.org/stable/_sources/auto_examples/release_highlights/plot_release_highlights_1_8_0.r... -
clustering.rst.txt
1, 1, 1] >>> labels_pred = [0, 0, 1, 1, 2, 2] >>>...= [0, 0, 0, 1, 1, 1] >>> labels_pred = [0, 0, 1, 1, 2, 2] >>>...scikit-learn.org/stable/_sources/modules/clustering.rst.txt -
plot_classifier_comparison.rst.txt
C=1, random_state=42), GaussianProcessClass(1.0 * RBF(1.0),...max_features=1, random_state=42 ), MLPClassifier(alpha=1, max_iter=1000,...scikit-learn.org/stable/_sources/auto_examples/classification/plot_classifier_comparison.rst.txt -
plot_kmeans_digits.rst.txt
1].min() - 1, reduced_data[:, 1].max() + 1 xx, yy =...reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1 y_min, y_max =...scikit-learn.org/stable/_sources/auto_examples/cluster/plot_kmeans_digits.rst.txt -
getting_started.rst.txt
dataset is easy array([1., 1., 1., 1., 1.]) Automatic parameter...ransform(X) array([[-1., 1.], [ 1., -1.]]) Sometimes, you want...scikit-learn.org/stable/_sources/getting_started.rst.txt