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simplebar.min.css
s:none;z-index:-1}.simplebar-track{z-index:1;position:absolu...1px;overflow:hidden;z-index:-1;padding:0;margin:0;pointer-ev...fess.codelibs.org/ja/_static/assets/vendor/simplebar/dist/simplebar.min.css -
prism-toolbar.css
toolbar { opacity: 1; } /* Separate line b/c rules...within > .toolbar { opacity: 1; } div.code-toolbar > .toolbar...fess.codelibs.org/ja/_static/assets/vendor/prismjs/plugins/toolbar/prism-toolbar.css -
prism.css
word-wrap: normal; line-height: 1.5; -moz-tab-size: 4; -o-tab-size:...fess.codelibs.org/ja/_static/assets/vendor/prismjs/themes/prism.css -
plot_release_highlights_1_4_0.py
1, 6, np.nan]).reshape(-1, 1) y = [0, 0, 1, 1] forest...3) disp.axes_[0, 0].set_xlim(-1, 1) disp.axes_[0, 0].legend() plt.show()...scikit-learn.org/stable/_downloads/c15cce0dbcd8722cb5638987eff985c0/plot_release_highlights_1_4_0.py -
plot_discretization_strategies.py
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/_downloads/43e84df0b93ff974da370e8da900f2ee/plot_discretization_strategie... -
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_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_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_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_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