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plot_kmeans_digits.ipynb
1].min() - 1, reduced_data[:, 1].max() + 1\nxx, yy =...reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1\ny_min, y_max =...scikit-learn.org/stable/_downloads/6bf322ce1724c13e6e0f8f719ebd253c/plot_kmeans_digits.ipynb -
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_pca_iris.ipynb
1].mean() + 1.5,\n X[y == label, 2].mean(),\n...np.choose(y, [1, 2, 0]).astype(float)\nax.scatter(X[:, 0], X[:, 1], X[:,...scikit-learn.org/stable/_downloads/46b6a23d83637bf0f381ce9d8c528aa2/plot_pca_iris.ipynb -
plot_classifier_comparison.ipynb
C=1, random_state=42),\n GaussianProcessClass(1.0 * RBF(1.0),...max_features=1, random_state=42\n ),\n MLPClassifier(alpha=1, max_iter=1000,...scikit-learn.org/stable/_downloads/3438aba177365cb595921cf18806dfa7/plot_classifier_comparison.ipynb -
plot_classifier_comparison.py
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/_downloads/2da0534ab0e0c8241033bcc2d912e419/plot_classifier_comparison.py -
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_multi_metric_evaluation.py
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/_downloads/dedbcc9464f3269f4f012f4bfc7d16da/plot_multi_metric_evaluation.py -
plot_kmeans_digits.py
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/_downloads/5a87b25ba023ee709595b8d02049f021/plot_kmeans_digits.py -
copybutton.css
3em; width: 1.7em; height: 1.7em; opacity: 0; transition:...stroke: currentColor; width: 1.5em; height: 1.5em; padding: 0.1em; }...scikit-learn.org/stable/_static/copybutton.css -
plot_pca_iris.py
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/_downloads/1168f82083b3e70f31672e7c33738f8d/plot_pca_iris.py