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affinity_propagation — scikit-learn 1.8.0 docum...
array ([[ 1 , 2 ], [ 1 , 4 ], [ 1 , 0 ], ... [ 4 , 2...>>> labels array([0, 0, 0, 1, 1, 1]) Gallery examples # Visualizing...scikit-learn.org/stable/modules/generated/sklearn.cluster.affinity_propagation.html -
GroupShuffleSplit — scikit-learn 1.8.0 document...
index=[0 1], group=[1 1] Fold 1: Train: index=[0 1 5 6 7], group=[1...shape = ( 8 , 1 )) >>> groups = np . array ([ 1 , 1 , 2 , 2 , 2...scikit-learn.org/stable/modules/generated/sklearn.model_selection.GroupShuffleSplit.html -
FeatureHasher — scikit-learn 1.8.0 documentation
-1., 0., -1., 0., 1.], [ 0., 0., 0., -1., 0., -1., 0., 0.],...0.], [ 0., -1., 0., 0., 0., 0., 0., 1.]]) fit ( X = None , y...scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.FeatureHasher.html -
max_error — scikit-learn 1.8.0 documentation
1 ] >>> y_pred = [ 4 , 2 , 7 , 1 ] >>> max_error...max_error ( y_true , y_pred ) 1.0 On this page This Page...scikit-learn.org/stable/modules/generated/sklearn.metrics.max_error.html -
PLSRegression — 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.PLSRegression.html -
SelectorMixin — scikit-learn 1.8.0 documentation
shape [ 1 ] ... return self ... def _get_support_mask..."x1", ..., "x(n_features_in_ - 1)"] . If input_features is an array-like,...scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectorMixin.html -
compute_class_weight — scikit-learn 1.8.0 docum...
compute_class_weight >>> y = [ 1 , 1 , 1 , 1 , 0 , 0 ] >>> compute_class_weight...unique ( y ), y = y ) array([1.5 , 0.75]) On this page This Page...scikit-learn.org/stable/modules/generated/sklearn.utils.class_weight.compute_class_weight.html -
check_is_fitted — scikit-learn 1.8.0 documentation
fit ([[ 1 , 2 ], [ 1 , 3 ]], [ 1 , 0 ]) LogisticRegression()...scikit-learn.org/stable/modules/generated/sklearn.utils.validation.check_is_fitted.html -
NMF — scikit-learn 1.8.0 documentation
array ([[ 1 , 1 ], [ 2 , 1 ], [ 3 , 1.2 ], [ 4 , 1 ], [ 5 , 0.8...version 1.4: Added 'auto' value. Changed in version 1.6: Default...scikit-learn.org/stable/modules/generated/sklearn.decomposition.NMF.html -
ElasticNetCV — scikit-learn 1.8.0 documentation
l1_ratio = 1 it is an L1 penalty. For 0 < l1_ratio < 1 , the penalty...(i.e. Ridge), as in [.1, .5, .7, .9, .95, .99, 1] . eps float, default=1e-3...scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNetCV.html