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shrunk_covariance — scikit-learn 1.8.0 document...
1 ) [source] # Calculate covariance...ndarray. shrinkage float, default=0.1 Coefficient in the convex combination...scikit-learn.org/stable/modules/generated/sklearn.covariance.shrunk_covariance.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 -
PLSCanonical — scikit-learn 1.8.0 documentation
1 , - 0.2 ], [ 0.9 , 1.1 ], [ 6.2 , 5.9 ],...intercept_ . Added in version 1.1. n_iter_ list of shape (n_components,)...scikit-learn.org/stable/modules/generated/sklearn.cross_decomposition.PLSCanonical.html -
cosine_similarity — scikit-learn 1.8.0 document...
[ 1 , 1 , 1 ]] >>> Y = [[ 1 , 0 , 0 ], [ 1 , 1 , 0 ]] >>>...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.cosine_similarity.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 -
fbeta_score — scikit-learn 1.8.0 documentation
1 , 2 , 0 , 1 , 2 ] >>> y_pred = [ 0 , 2 , 1 , 0 , 0...beta > 1 gives more weight to recall, while beta < 1 favors precision....scikit-learn.org/stable/modules/generated/sklearn.metrics.fbeta_score.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 -
AdditiveChi2Sampler — scikit-learn 1.8.0 docume...
transformed into 2*sample_steps-1 features, where sample_steps is...values of sample_steps include 1, 2 and 3. Optimal choices for...scikit-learn.org/stable/modules/generated/sklearn.kernel_approximation.AdditiveChi2Sampler.html