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pairwise_distances_argmin_min — scikit-learn 1....
[ 1 , 1 , 1 ]] >>> Y = [[ 1 , 0 , 0 ], [ 1 , 1 , 0 ]] >>>...argmin array([0, 1]) >>> distances array([1., 1.]) On this page...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise_distances_argmin_min.html -
FeatureHasher — scikit-learn 1.8.0 documentation
-1., 0., -1., 0., 1.], [ 0., 0., 0., -1., 0., -1., 0., 0.],...0.], [ 0., -1., 0., 0., 0., 0., 0., 1.]]) fit ( X = None , y...scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.FeatureHasher.html -
affinity_propagation — scikit-learn 1.8.0 docum...
array ([[ 1 , 2 ], [ 1 , 4 ], [ 1 , 0 ], ... [ 4 , 2...>>> labels array([0, 0, 0, 1, 1, 1]) Gallery examples # Visualizing...scikit-learn.org/stable/modules/generated/sklearn.cluster.affinity_propagation.html -
balanced_accuracy_score — scikit-learn 1.8.0 do...
1 , 0 , 0 , 1 , 0 ] >>> y_pred = [ 0 , 1 , 0 , 0 ,...each class. The best value is 1 and the worst value is 0 when...scikit-learn.org/stable/modules/generated/sklearn.metrics.balanced_accuracy_score.html -
add_dummy_feature — scikit-learn 1.8.0 document...
1 ], [ 1 , 0 ]]) array([[1., 0., 1.], [1., 1., 0.]])...add_dummy_feature ( X , value = 1.0 ) [source] # Augment dataset...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.add_dummy_feature.html -
normalized_mutual_info_score — scikit-learn 1.8...
1 , 1 ], [ 0 , 0 , 1 , 1 ]) 1.0 >>> normalized_mutual_info_score...ore ([ 0 , 0 , 1 , 1 ], [ 1 , 1 , 0 , 0 ]) 1.0 If classes members...scikit-learn.org/stable/modules/generated/sklearn.metrics.normalized_mutual_info_score.html -
label_ranking_average_precision_score — scikit-...
1 ], [ 1 , 0.2 , 0.1 ]]) >>> label_ranki...y_true = np . array ([[ 1 , 0 , 0 ], [ 0 , 0 , 1 ]]) >>> y_score =...scikit-learn.org/stable/modules/generated/sklearn.metrics.label_ranking_average_precision_score.html -
pair_confusion_matrix — scikit-learn 1.8.0 docu...
1 , 1 ], [ 1 , 1 , 0 , 0 ]) array([[8,...pair_confusion_matrix ([ 0 , 0 , 1 , 2 ], [ 0 , 0 , 1 , 1 ]) array([[8, 2],...scikit-learn.org/stable/modules/generated/sklearn.metrics.cluster.pair_confusion_matrix.html -
BayesianGaussianMixture — scikit-learn 1.8.0 do...
array ([[ 1 , 2 ], [ 1 , 4 ], [ 1 , 0 ], [ 4 , 2 ], [...mixtures”. Bayesian analysis 1.1 Examples >>> import numpy as...scikit-learn.org/stable/modules/generated/sklearn.mixture.BayesianGaussianMixture.html -
BiclusterMixin — scikit-learn 1.8.0 documentation
array ([[ 1 , 1 ], [ 2 , 1 ], [ 1 , 0 ], ... [ 4 , 7...np . ones ( shape = ( 1 , X . shape [ 1 ]), dtype = bool ) ......scikit-learn.org/stable/modules/generated/sklearn.base.BiclusterMixin.html