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plot_classifier_comparison.rst.txt
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/_sources/auto_examples/classification/plot_classifier_comparison.rst.txt -
7.8. Pairwise metrics, Affinities and Kernels —...
for all a and b 2. d ( a , b ) == 0 , if and only...pairwise_kernels >>> X = np . array ([[ 2 , 3 ], [ 3 , 5 ], [ 5 , 8 ]])...scikit-learn.org/stable/modules/metrics.html -
non_negative_factorization — scikit-learn 1.8.0...
||H||_{Fro}^2,\end{aligned}\end{align} \] where \(||A||_{Fro}^2 = \sum_{i,j}...array ([[ 1 , 1 ], [ 2 , 1 ], [ 3 , 1.2 ], [ 4 , 1 ], [ 5 , 0.8...scikit-learn.org/stable/modules/generated/sklearn.decomposition.non_negative_factorization.html -
indexable — scikit-learn 1.8.0 documentation
2 , 3 ], np . array ([ 2 , 3 , 4 ]), None ,...indexable ( * iterables ) [[1, 2, 3], array([2, 3, 4]), None, <...Sparse...dtype...scikit-learn.org/stable/modules/generated/sklearn.utils.indexable.html -
Understanding the decision tree structure — sci...
3] = 2.4) > 0.800000011920929) decision node 2 : (X_test[0,...min_samples_split: int or float, default=2 The minimum number of samples...scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html -
homogeneity_completeness_v_measure — scikit-lea...
2 , 2 ], [ 0 , 0 , 1 , 2 , 2 , 2 ] >>> homogen...scikit-learn.org/stable/modules/generated/sklearn.metrics.homogeneity_completeness_v_measure.html -
mean_pinball_loss — scikit-learn 1.8.0 document...
2 , 3 ] >>> mean_pinball_loss ( y_true , [ 0 , 2 , 3 ],...mean_pinball_loss ( y_true , [ 1 , 2 , 4 ], alpha = 0.1 ) 0.3... >>>...scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_pinball_loss.html -
resample — scikit-learn 1.8.0 documentation
[ 2. , 1. ], [ 0. , 0. ]]) >>>...>>> y = np . array ([ 0 , 1 , 2 ]) >>> from scipy.sparse...scikit-learn.org/stable/modules/generated/sklearn.utils.resample.html -
EllipticEnvelope — scikit-learn 1.8.0 documenta...
(n_samples + n_features + 1) / 2 * n_samples . Range is (0, 1)....with n_samples > n_features ** 2 . References [ 1 ] Rousseeuw,...scikit-learn.org/stable/modules/generated/sklearn.covariance.EllipticEnvelope.html -
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