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ndcg_score — scikit-learn 1.8.0 documentation
1.1 , 1. , .5 , .0 ]]) >>> ndcg_score...scores = np . asarray ([[ 1 , 0 , 0 , 0 , 1 ]]) >>> # by default ties...scikit-learn.org/stable/modules/generated/sklearn.metrics.ndcg_score.html -
PolynomialFeatures — scikit-learn 1.8.0 documen...
fit_transform ( X ) array([[ 1., 0., 1., 0., 0., 1.], [ 1., 2., 3., 4., 6.,...) array([[ 1., 0., 1., 0.], [ 1., 2., 3., 6.], [ 1., 4., 5., 20.]])...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html -
chi2_kernel — scikit-learn 1.8.0 documentation
[ 1 , 1 , 1 ]] >>> Y = [[ 1 , 0 , 0 ], [ 1 , 1 , 0 ]] >>>...chi2_kernel ( X , Y = None , gamma = 1.0 ) [source] # Compute the exponential...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.chi2_kernel.html -
mutual_info_score — scikit-learn 1.8.0 document...
1 , 1 , 0 , 1 , 0 ] >>> labels_pred = [ 0 , 1 , 0 , 0...as: \[MI(U,V)=\sum_{i=1}^{|U|} \sum_{j=1}^{|V|} \frac{|U_i\cap...scikit-learn.org/stable/modules/generated/sklearn.metrics.mutual_info_score.html -
paired_cosine_distances — scikit-learn 1.8.0 do...
[ 1 , 1 , 1 ]] >>> Y = [[ 1 , 0 , 0 ], [ 1 , 1 , 0 ]] >>>...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.paired_cosine_distances.html -
LeavePOut — scikit-learn 1.8.0 documentation
3] Test: index=[0 1] Fold 1: Train: index=[1 3] Test: index=[0...index=[1 2] Fold 4: Train: index=[0 2] Test: index=[1 3] Fold...scikit-learn.org/stable/modules/generated/sklearn.model_selection.LeavePOut.html -
RepeatedKFold — scikit-learn 1.8.0 documentation
array ([[ 1 , 2 ], [ 3 , 4 ], [ 1 , 2 ], [ 3 , 4 ]])...>>> y = np . array ([ 0 , 0 , 1 , 1 ]) >>> rkf = RepeatedKFold (...scikit-learn.org/stable/modules/generated/sklearn.model_selection.RepeatedKFold.html -
chi2 — scikit-learn 1.8.0 documentation
array ([[ 1 , 1 , 3 ], ... [ 0 , 1 , 5 ], ... [ 5 , 4 , 1 ], ......y = np . array ([ 1 , 1 , 0 , 0 , 2 , 2 ]) >>> chi2_stats , p_values...scikit-learn.org/stable/modules/generated/sklearn.feature_selection.chi2.html -
RadiusNeighborsClassifier — scikit-learn 1.8.0 ...
() array([[1., 0., 1.], [0., 1., 0.], [1., 0., 1.]]) score (...[[ 0 ], [ 1 ], [ 2 ], [ 3 ]] >>> y = [ 0 , 0 , 1 , 1 ] >>> from...scikit-learn.org/stable/modules/generated/sklearn.neighbors.RadiusNeighborsClassifier.html -
MaxAbsScaler — scikit-learn 1.8.0 documentation
-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