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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 -
custom.css
font-weight: 500; margin-bottom: 1.5rem; } .bd-article img { height:...height: unset !important; padding: 1%; } .bd-article li.toctree-l1...scikit-learn.org/stable/_static/styles/custom.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 -
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_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 -
jupyterlite_sphinx.css
jupyterlite_sphinx_iframe { z-index: 1; position: relative; border-style:...translateY(-50%) translateX(-50%) scale(1.2); } .jupyterlite_sphinx_try_it_button_clicked...scikit-learn.org/stable/_static/jupyterlite_sphinx.css -
index.css
padding-left: 1.3rem !important; padding-right: 1.3rem !important;...html[data-theme="light"] { --sk-landing-bg-1: var(--sk-cyan-shades-3); --sk-landing-bg-2:...scikit-learn.org/stable/_static/styles/index.css -
user_guide.rst.txt
toctree:: :numbered: :maxdepth: 1 metadata_routing.rst...scikit-learn.org/stable/_sources/user_guide.rst.txt -
plot_multi_metric_evaluation.ipynb
1)\n\n# Get the regular numpy array...+ sample_score_std,\n alpha=0.1 if sample == \"test\" else 0,\n...scikit-learn.org/stable/_downloads/f57e1ee55d4c7a51949d5c26b3af07bb/plot_multi_metric_evaluation....