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plot_multi_metric_evaluation.rst.txt
1) # Get the regular numpy array...sample_score_mean + sample_score_std, alpha=0.1 if sample == "test" else 0, color=color,...scikit-learn.org/stable/_sources/auto_examples/model_selection/plot_multi_metric_evaluation.rst.txt -
plot_discretization_strategies.rst.txt
8]]) centers_1 = np.array([[0, 0], [3, 1]]) # construct the...len(strategies) + 1, i) ax.scatter(X[:, 0], X[:, 1], edgecolors="k")...scikit-learn.org/stable/_sources/auto_examples/preprocessing/plot_discretization_strategies.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 -
user_guide.rst.txt
toctree:: :numbered: :maxdepth: 1 metadata_routing.rst...scikit-learn.org/stable/_sources/user_guide.rst.txt -
test.mm
1"> <node TEXT="Lorem ipsum. (ロ...raw.githubusercontent.com/codelibs/fess-testdata/master/xml/test.mm -
plot_pca_iris.rst.txt
1].mean() + 1.5, X[y == label, 2].mean(),...np.choose(y, [1, 2, 0]).astype(float) ax.scatter(X[:, 0], X[:, 1], X[:,...scikit-learn.org/stable/_sources/auto_examples/decomposition/plot_pca_iris.rst.txt -
plot_adaboost_regression.rst.txt
random_state=rng ) regr_1.fit(X, y) regr_2.fit(X, y) y_1 = regr_1.predict(X)...plt.plot(X, y_1, color=colors[1], label="n_estimators=1", linewidth=2)...scikit-learn.org/stable/_sources/auto_examples/ensemble/plot_adaboost_regression.rst.txt