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KernelPCA — scikit-learn 1.8.0 documentation
coef0 = 1 , kernel_params = None , alpha = 1.0 , fit_inverse_transform...means 1 unless in a joblib.parallel_backend context. -1 means...scikit-learn.org/stable/modules/generated/sklearn.decomposition.KernelPCA.html -
LabelBinarizer — scikit-learn 1.8.0 docum...
array([[1, 0, 0], [0, 1, 0], [0, 0, 1], [0, 1, 0]]) fit ( y )...fit ( np . array ([[ 0 , 1 , 1 ], [ 1 , 0 , 0 ]])) LabelBinarizer()...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelBinarizer.html -
MaxAbsScaler — scikit-learn 1.8.0 documen...
-1. , 1. ], [ 1. , 0. , 0. ], [ 0. , 1. , -0.5]]) fit...= [[ 1. , - 1. , 2. ], ... [ 2. , 0. , 0. ], ... [ 0. , 1. , -...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MaxAbsScaler.html -
fbeta_score — scikit-learn 1.8.0 document...
1 , 2 , 0 , 1 , 2 ] >>> y_pred = [ 0 , 2 , 1 , 0...beta > 1 gives more weight to recall, while beta < 1 favors...scikit-learn.org/stable/modules/generated/sklearn.metrics.fbeta_score.html -
NearestNeighbors — scikit-learn 1.8.0 doc...
() array([[1., 0., 1.], [0., 1., 1.], [1., 0., 1.]]) radius_neighbors...() array([[1., 0., 1.], [0., 1., 0.], [1., 0., 1.]]) set_params...scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html -
d2_brier_score — scikit-learn 1.8.0 docum...
y_true in {-1, 1} or {0, 1}, pos_label defaults to 1; else if y_true...explained. Best possible score is 1.0 and it can be negative because...scikit-learn.org/stable/modules/generated/sklearn.metrics.d2_brier_score.html -
polynomial_kernel — scikit-learn 1.8.0 do...
[ 1 , 1 , 1 ]] >>> Y = [[ 1 , 0 , 0 ], [ 1 , 1 , 0..., degree = 2 ) array([[1. , 1. ], [1.77, 2.77]]) On this page...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.polynomial_kernel.html -
PredefinedSplit — scikit-learn 1.8.0 docu...
1 , 1 ]) >>> test_fold = [ 0 , 1 , - 1 , 1 ] >>>...PredefinedSplit(test_fold=array([ 0, 1, -1, 1])) >>> for i , (...scikit-learn.org/stable/modules/generated/sklearn.model_selection.PredefinedSplit.html -
paired_manhattan_distances — scikit-learn...
array ([[ 1 , 1 , 0 ], [ 0 , 1 , 0 ], [ 0 , 0 , 1 ]]) >>>...calculated between (X[0], Y[0]), (X[1], Y[1]), …, (X[n_samples], Y[n_samples])....scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.paired_manhattan_distances.html -
RBFSampler — scikit-learn 1.8.0 documenta...
[ 1 , 1 ], [ 1 , 0 ], [ 0 , 1 ]] >>> y...version 1.2: The option "scale" was added in 1.2. n_components...scikit-learn.org/stable/modules/generated/sklearn.kernel_approximation.RBFSampler.html