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jaccard_score — scikit-learn 1.8.0 docume...
1 , 1 ], ... [ 1 , 1 , 0 ]]) >>>...y_pred = np . array ([[ 1 , 1 , 1 ], ... [ 1 , 0 , 0 ]]) In the binary...scikit-learn.org/stable/modules/generated/sklearn.metrics.jaccard_score.html -
StratifiedGroupKFold — scikit-learn 1.8.0...
1 , 1 , 1 , 1 , 1 , 1 , 0 , 0 , 0 , 0 , 0...Train: index=[ 0 1 2 3 7 8 9 10 11 15 16] group=[1 1 2 2 4 5 5 5 5...scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedGroupKFold.html -
paired_distances — scikit-learn 1.8.0 doc...
1 ], [ 1 , 1 ]] >>> Y = [[ 0 , 1 ], [ 2 , 1 ]] >>>...distances between (X[0], Y[0]), (X[1], Y[1]), etc… Read more in the User...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.paired_distances.html -
d2_absolute_error_score — scikit-learn 1....
1 ], [ - 1 , 1 ], [ 7 , - 6 ]] >>>...User Guide . Added in version 1.1. Parameters : y_true array-like...scikit-learn.org/stable/modules/generated/sklearn.metrics.d2_absolute_error_score.html -
kneighbors_graph — scikit-learn 1.8.0 doc...
() array([[1., 0., 1.], [0., 1., 1.], [1., 0., 1.]]) Gallery...means 1 unless in a joblib.parallel_backend context. -1 means...scikit-learn.org/stable/modules/generated/sklearn.neighbors.kneighbors_graph.html -
adjusted_mutual_info_score — scikit-learn...
1 , 1 ], [ 0 , 0 , 1 , 1 ]) 1.0 >>> ...ore ([ 0 , 0 , 1 , 1 ], [ 1 , 1 , 0 , 0 ]) 1.0 If classes members...scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_mutual_info_score.html -
check_symmetric — scikit-learn 1.8.0 docu...
1 , 2 ], [ 1 , 0 , 1 ], [ 2 , 1 , 0 ]]) >>>...symmetric_array ) array([[0, 1, 2], [1, 0, 1], [2, 1, 0]]) >>>...scikit-learn.org/stable/modules/generated/sklearn.utils.validation.check_symmetric.html -
radius_neighbors_graph — scikit-learn 1.8...
() array([[1., 0., 1.], [0., 1., 0.], [1., 0., 1.]]) On this...means 1 unless in a joblib.parallel_backend context. -1 means...scikit-learn.org/stable/modules/generated/sklearn.neighbors.radius_neighbors_graph.html -
shuffle — scikit-learn 1.8.0 documentation
1.], [1., 0.]]) >>> y array([2, 1, 0]) >>>...>>> X = np . array ([[ 1. , 0. ], [ 2. , 1. ], [ 0. , 0. ]]) >>>...scikit-learn.org/stable/modules/generated/sklearn.utils.shuffle.html -
RegressorChain — scikit-learn 1.8.0 docum...
= [[ 1 , 0 ], [ 0 , 1 ], [ 1 , 1 ]], [[ 0 , 2 ], [ 1 , 1 ], [...order = [ 0 , 1 , 2 , ... , Y . shape [ 1 ] - 1 ] The order of...scikit-learn.org/stable/modules/generated/sklearn.multioutput.RegressorChain.html