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PredefinedSplit — scikit-learn 1.7.2 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 -
polynomial_kernel — scikit-learn 1.7.2 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 -
KDTree — scikit-learn 1.7.2 documentation
query ( X [: 1 ], k = 3 ) >>> print...indices of 3 closest neighbors [0 3 1] >>> print ( dist ) #...scikit-learn.org/stable/modules/generated/sklearn.neighbors.KDTree.html -
homogeneity_completeness_v_measure — scik...
1 , 1 , 2 , 2 ], [ 0 , 0 , 1 , 2 , 2 , 2 ] >>>...float Score between 0.0 and 1.0. 1.0 stands for perfectly homogeneous...scikit-learn.org/stable/modules/generated/sklearn.metrics.homogeneity_completeness_v_measure.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 -
LeavePGroupsOut — scikit-learn 1.7.2 docu...
Test: index=[0 1], group=[1 2] Fold 1: Train: index=[1], group=[2]...array ([ 1 , 2 , 1 ]) >>> groups = np . array ([ 1 , 2 ,...scikit-learn.org/stable/modules/generated/sklearn.model_selection.LeavePGroupsOut.html -
d2_pinball_score — scikit-learn 1.7.2 doc...
Added in version 1.1. Parameters : y_true array-like...y_true = [ 1 , 2 , 3 ] >>> y_pred = [ 1 , 3 , 3 ] >>>...scikit-learn.org/stable/modules/generated/sklearn.metrics.d2_pinball_score.html -
make_moons — scikit-learn 1.7.2 documenta...
) The integer labels (0 or 1) for class membership of each...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html -
OutlierMixin — scikit-learn 1.7.2 documen...
fit_predict ( X ) array([1., 1., 1.]) fit_predict ( X , y = None...labels for X. Returns -1 for outliers and 1 for inliers. Parameters...scikit-learn.org/stable/modules/generated/sklearn.base.OutlierMixin.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