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auto_examples_python.zip
- l / 2.0) ** 2 + (y - l / 2.0) ** 2 < (l / 2.0) ** 2 mask =...+ 1, figsize=(4 * 2.2, n_classifiers * 2.2), ) evaluation_results...scikit-learn.org/stable/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip -
plot_classifier_comparison.zip
make_classification( n_features=2, n_redundant=0, n_informative=2, random_state=1,...rng = np.random.RandomState(2) X += 2 * rng.uniform(size=X.shape)...scikit-learn.org/stable/_downloads/ce35bcc69acbd491cf7ac77fa17889d5/plot_classifier_comparison.zip -
styles.css
mr-2{margin-right:calc(var(--spacing)*2)}.mb-2{margin-b...spacing)*2)}.ml-2{margin-left:calc(var(--spacing)*2)}.ml-4{m...www.elastic.co/docs/_static/styles.css -
plot_multi_metric_evaluation.ipynb
param_grid={\"min_samples_split\": range(2, 403, 20)},\n scoring=scoring,\n...scoring=scoring,\n refit=\"AUC\",\n n_jobs=2,\n return_train_score=True,\n)\ngs.fit(X,...scikit-learn.org/stable/_downloads/f57e1ee55d4c7a51949d5c26b3af07bb/plot_multi_metric_evaluation.... -
plot_kmeans_digits.zip
PCA` to project into a # 2-dimensional space and plot the...reduced_data = PCA(n_components=2).fit_transform(data) kmeans =...scikit-learn.org/stable/_downloads/1393861b58df827d4c681b80a5be2472/plot_kmeans_digits.zip -
plot_multi_metric_evaluation.zip
range(2, 403, 20)}, scoring=scoring, refit="AUC", n_jobs=2, re...ax.plot( [ X_axis[best_index], ] * 2, [0, best_score], linestyle="-.",...scikit-learn.org/stable/_downloads/535778bfbc9b4881da3e662bc2ea8484/plot_multi_metric_evaluation.zip -
pydata-sphinx-theme.css
g-2,.gx-2{--bs-gutter-x:0.5rem}.g-2,.gy-2{--bs-gutte....g-sm-2,.gx-sm-2{--bs-gutter-x:0.5rem}.g-sm-2,.gy-sm-2{--bs-...scikit-learn.org/stable/_static/styles/pydata-sphinx-theme.css -
plot_multi_metric_evaluation.py
range(2, 403, 20)}, scoring=scoring, refit="AUC", n_jobs=2, re...ax.plot( [ X_axis[best_index], ] * 2, [0, best_score], linestyle="-.",...scikit-learn.org/stable/_downloads/dedbcc9464f3269f4f012f4bfc7d16da/plot_multi_metric_evaluation.py -
plot_discretization_strategies.zip
[2, 4], [8, 8]]) centers_1 = np.array([[0,...form(-3, 3, size=(n_samples, 2)), make_blobs( n_samples=[ n_samples...scikit-learn.org/stable/_downloads/7b16734166ab4280e940d7fb89dd6113/plot_discretization_strategie... -
plot_discretization_strategies.py
[2, 4], [8, 8]]) centers_1 = np.array([[0,...form(-3, 3, size=(n_samples, 2)), make_blobs( n_samples=[ n_samples...scikit-learn.org/stable/_downloads/43e84df0b93ff974da370e8da900f2ee/plot_discretization_strategie...