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GaussianMixture — scikit-learn 1.8.0 documentation
array ([[ 1 , 2 ], [ 1 , 4 ], [ 1 , 0 ], [ 10 , 2 ], [ 10 , 4 ],.... predict ([[ 0 , 0 ], [ 12 , 3 ]]) array([1, 0]) For a comparison...scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html -
StratifiedGroupKFold — scikit-learn 1.8.0 docum...
([ 0 , 0 , 1 , 1 , 1 , 1 , 1 , 1 , 0 , 0 , 0 , 0 , 0 , 0 , 0...Fold 0: Train: index=[ 0 1 2 3 7 8 9 10 11 15 16] group=[1 1 2...scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedGroupKFold.html -
adjusted_mutual_info_score — scikit-learn 1.8.0...
_score ([ 0 , 0 , 1 , 1 ], [ 0 , 0 , 1 , 1 ]) 1.0 >>> adjust..._score ([ 0 , 0 , 1 , 1 ], [ 1 , 1 , 0 , 0 ]) 1.0 If classes...scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_mutual_info_score.html -
homogeneity_score — scikit-learn 1.8.0 document...
homogeneity_score ([ 0 , 0 , 1 , 1 ], [ 1 , 1 , 0 , 0 ]) 1.0 Non-perfect...homogeneity_score ([ 0 , 0 , 1 , 1 ], [ 0 , 1 , 0 , 1 ])) 0.0... >>> print...scikit-learn.org/stable/modules/generated/sklearn.metrics.homogeneity_score.html -
haversine_distances — scikit-learn 1.8.0 docume...
kilometers array([[ 0. , 11099.54035582], [11099.54035582, 0. ]]) On this...Earth surface, with a less than 1% error on average. Examples We...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.haversine_distances.html -
label_ranking_loss — scikit-learn 1.8.0 documen...
[[ 1 , 0 , 0 ], [ 0 , 0 , 1 ]] >>> y_score = [[ 0.75 , 0.5 ,..., 1 ], [ 1 , 0.2 , 0.1 ]] >>> label_ranking_loss ( y_true , y_score...scikit-learn.org/stable/modules/generated/sklearn.metrics.label_ranking_loss.html -
manhattan_distances — scikit-learn 1.8.0 docume...
[[ 1 , 2 ], [ 0 , 3 ]]) array([[0., 2.], [4., 4.]]) On...]]) array([[1.]]) >>> manhattan_distances ([[ 1 , 2 ], [ 3 ,...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.manhattan_distances.html -
sklearn.naive_bayes — scikit-learn 1.8.0 docume...
Naive Bayes algorithms. These are supervised learning methods based on applying Bayes’ theorem with strong (naive) feature independence assumptions. User guide. See the Naive Bayes section for furt...scikit-learn.org/stable/api/sklearn.naive_bayes.html -
quantile_transform — scikit-learn 1.8.0 documen...
scale = 0.25 , size = ( 25 , 1 )), axis = 0 ) >>> quantile_transform...quantile_transform ( X , * , axis = 0 , n_quantiles = 1000 , output_distribution...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.quantile_transform.html -
OneHotEncoder — scikit-learn 1.8.0 documentation
([[ 0 , 1 , 1 , 0 , 0 ], [ 0 , 0 , 0 , 1 , 0 ]]) array([['Male',...0., 0.], [0., 1., 0., 0., 0.]]) >>> enc . inverse_transform ([[...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html