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plot_discretization_strategies.rst.txt
meshgrid( np.linspace(X[:, 0].min(), X[:, 0].max(), 300), np.linspace(X[:,...len(strategies) + 1, i) ax.scatter(X[:, 0], X[:, 1], edgecolors="k") if ds_cnt...scikit-learn.org/stable/_sources/auto_examples/preprocessing/plot_discretization_strategies.rst.txt -
feature_selection.rst.txt
= [[0, 0, 1], [0, 1, 0], [1, 0, 0], [0, 1, 1], [0, 1, 0], [0,....8))) >>> sel.fit_transform(X) array([[0, 1], [1, 0], [0, 0], [1,...scikit-learn.org/stable/_sources/modules/feature_selection.rst.txt -
model_evaluation.rst.txt
evaluation (`mean_pinball_loss(..., alpha=0.99)` - we apologize...`QuantileRegressor(quantile=0.99)`. .. rubric:: References .. [Gneiting2007]...scikit-learn.org/stable/_sources/modules/model_evaluation.rst.txt -
cross_validation.rst.txt
((90, 4), (90,)) >>> X_test.shape, y_test.shape ((60, 4), (60,))...cross_val_score(clf, X, y, cv=5) >>> scores array([0.96, 1. , 0.96, 0.96,...scikit-learn.org/stable/_sources/modules/cross_validation.rst.txt -
linear_model.rst.txt
linear_model.Ridge(alpha=.5) >>> reg.fit([[0, 0], [0, 0], [1, 1]], [0,...value. .. math:: \hat{y}(w, x) = w_0 + w_1 x_1 + ... + w_p x_p...scikit-learn.org/stable/_sources/modules/linear_model.rst.txt -
plot_multi_metric_evaluation.rst.txt
for sample, style in (("train", "--"), ("test", "-")): sample_score_mean...plt.ylabel("Score") ax = plt.gca() ax.set_xlim(0, 402) ax.set_ylim(0.73,...scikit-learn.org/stable/_sources/auto_examples/model_selection/plot_multi_metric_evaluation.rst.txt -
governance.rst.txt
way, they are a contributor. Core Contributors ---------- All...relevant to their roles. Contributors ---------- Contributors are community...scikit-learn.org/stable/_sources/governance.rst.txt -
feature_extraction.rst.txt
array([[ 1., 0., 0., 33.], [ 0., 1., 0., 12.], [ 0., 0., 1., 18.]])...1.000e+00, 0.000e+00, 1.000e+00, 2.003e+03], [1.000e+00, 0.000e+00,...scikit-learn.org/stable/_sources/modules/feature_extraction.rst.txt -
user_guide.rst.txt
.. _user_guide: ========== User Guide ========== .. toctree::...data_transforms.rst datasets.rst computing.rst model_persistence.rst common_pitfalls.rst...scikit-learn.org/stable/_sources/user_guide.rst.txt -
plot_classifier_comparison.rst.txt
KNeighborsClassifier(3), SVC(kernel="linear", C=0.025, random_state=42), SVC(gamma=2,...Process", "Decision Tree", "Random Forest", "Neural Net", "AdaBoost",...scikit-learn.org/stable/_sources/auto_examples/classification/plot_classifier_comparison.rst.txt