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type_of_target — scikit-learn 1.8.0 docum...
>>> type_of_target ([ 1 , - 1 , - 1 , 1 ]) 'binary' >>>...type_of_target ( np . array ([[ 0 , 1 ], [ 1 , 1 ]])) 'multilabel-indicator'...scikit-learn.org/stable/modules/generated/sklearn.utils.multiclass.type_of_target.html -
RadiusNeighborsRegressor — scikit-learn 1...
() array([[1., 0., 1.], [0., 1., 0.], [1., 0., 1.]]) score (...], [ 1 ], [ 2 ], [ 3 ]] >>> y = [ 0 , 0 , 1 , 1 ] >>>...scikit-learn.org/stable/modules/generated/sklearn.neighbors.RadiusNeighborsRegressor.html -
SVC — scikit-learn 1.8.0 documentation
array ([[ - 1 , - 1 ], [ - 2 , - 1 ], [ 1 , 1 ], [ 2 , 1 ]]) >>>...default=-1 Hard limit on iterations within solver, or -1 for no...scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html -
minmax_scale — scikit-learn 1.8.0 documen...
1 , 2 ], [ - 1 , 0 , 1 ]] >>> minmax_scale...independently array([[0., 1., 1.], [1., 0., 0.]]) >>>...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.minmax_scale.html -
fowlkes_mallows_score — scikit-learn 1.8....
1 , 1 ], [ 0 , 0 , 1 , 1 ]) 1.0 >>> ...fowlkes_mallows_score ([ 0 , 0 , 1 , 1 ], [ 1 , 1 , 0 , 0 ]) 1.0 If classes members...scikit-learn.org/stable/modules/generated/sklearn.metrics.fowlkes_mallows_score.html -
paired_euclidean_distances — scikit-learn...
[ 1 , 1 , 1 ]] >>> Y = [[ 1 , 0 , 0 ], [ 1 , 1 , 0...paired_euclidean_distances ( X , Y ) array([1., 1.]) On this page This Page...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.paired_euclidean_distances.html -
make_sparse_spd_matrix — scikit-learn 1.8...
array([[1., 0., 0., 0.], [0., 1., 0., 0.], [0., 0., 1., 0.], [0.,...elements all 1. smallest_coef float, default=0.1 The value of...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_sparse_spd_matrix.html -
f1_score — scikit-learn 1.8.0 documentation
[ 1 , 1 , 1 ], [ 0 , 1 , 1 ]] >>> y_pred...= [[ 0 , 0 , 0 ], [ 1 , 1 , 1 ], [ 1 , 1 , 0 ]] >>>...scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html -
Binarizer — scikit-learn 1.8.0 documentation
( X ) array([[1., 0., 1.], [1., 0., 0.], [0., 1., 0.]]) fit (...= [[ 1. , - 1. , 2. ], ... [ 2. , 0. , 0. ], ... [ 0. , 1. , -...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Binarizer.html -
confusion_matrix — scikit-learn 1.8.0 doc...
1 , 0 , 1 ], [ 1 , 1 , 1 , 0 ]) . ravel ()...) array([[2, 0, 0], [0, 0, 1], [1, 0, 2]]) >>> y_true...scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html