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sg_gallery-rendered-html.css
margin: 1.08em 0 0 0; font-weight: bold; line-height: 1.0; } .rendered_html....rendered_html h2 { font-size: 157.1%; margin: 1.27em 0 0 0; font-weight:...scikit-learn.org/stable/_static/sg_gallery-rendered-html.css -
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_discretization_strategies.ipynb
8]])\ncenters_1 = np.array([[0, 0], [3, 1]])\n\n# construct...len(strategies) + 1, i)\n ax.scatter(X[:, 0], X[:, 1], edgecolors=\"k\")\n...scikit-learn.org/stable/_downloads/adc9be3b7acc279025dad9ee4ce92038/plot_discretization_strategie... -
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_adaboost_regression.py
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/_downloads/2da78c80da33b4e0d313b0a90b923ec8/plot_adaboost_regression.py -
plot_adaboost_regression.ipynb
\nregr_1.fit(X, y)\nregr_2.fit(X, y)\n\ny_1 = regr_1.predict(X)\ny_2...samples\")\nplt.plot(X, y_1, color=colors[1], label=\"n_estimators=1\", linew...scikit-learn.org/stable/_downloads/38e826c9e3778d7de78b2fc671fd7903/plot_adaboost_regression.ipynb -
sg_gallery.css
sphinx themes Tested for Sphinx 1.3.1 for all themes: default, alabaster,...--sg-download-a-hover-box-shadow-1: rgba(255, 255, 255, 0.1); --sg-download-...scikit-learn.org/stable/_static/sg_gallery.css -
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_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