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SparseCoder — scikit-learn 1.7.1 documentation
1 , 0 ], ... [ - 1 , - 1 , 2 ], ... [ 1 , 1 , 1 ], ......>>> X = np . array ([[ - 1 , - 1 , - 1 ], [ 0 , 0 , 3 ]]) >>> dictionary...scikit-learn.org/stable/modules/generated/sklearn.decomposition.SparseCoder.html -
StratifiedShuffleSplit — scikit-learn 1.7.1 doc...
array ([[ 1 , 2 ], [ 3 , 4 ], [ 1 , 2 ], [ 3 , 4 ], [ 1 , 2 ], [...np . array ([ 0 , 0 , 0 , 1 , 1 , 1 ]) >>> sss = StratifiedShuffleSpl...scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html -
PowerTransformer — scikit-learn 1.7.1 documenta...
data )) [[-1.316 -0.707] [ 0.209 -0.707] [ 1.106 1.414]] fit (...= ( X * lambda_ + 1 ) ** ( 1 / lambda_ ) - 1 elif X < 0 and lambda_...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PowerTransformer.html -
OrdinalEncoder — scikit-learn 1.7.1 documentation
inverse_transform ([[ 1 , 0 ], [ 0 , 1 ]]) array([['Male', 1], ['Female',... =- 1 ) . fit_transform ( X ) array([[ 1., 0.], [ 0., 1.], [...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OrdinalEncoder.html -
ClassifierMixin — scikit-learn 1.7.1 documentation
predict ( X ) array([1, 1, 1]) >>> estimator . score ( X...MyEstimator ( param = 1 ) >>> X = np . array ([[ 1 , 2 ], [ 2 , 3 ],...scikit-learn.org/stable/modules/generated/sklearn.base.ClassifierMixin.html -
RepeatedStratifiedKFold — scikit-learn 1.7.1 do...
array ([[ 1 , 2 ], [ 3 , 4 ], [ 1 , 2 ], [ 3 , 4 ]])...>>> y = np . array ([ 0 , 0 , 1 , 1 ]) >>> rskf = RepeatedStratifiedKF...scikit-learn.org/stable/modules/generated/sklearn.model_selection.RepeatedStratifiedKFold.html -
GroupShuffleSplit — scikit-learn 1.7.1 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 -
ElasticNetCV — scikit-learn 1.7.1 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 -
FeatureHasher — scikit-learn 1.7.1 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 -
NMF — scikit-learn 1.7.1 documentation
array ([[ 1 , 1 ], [ 2 , 1 ], [ 3 , 1.2 ], [ 4 , 1 ], [ 5 , 0.8...n\_samples * ||vec(H)||_1\\ &+ 0.5 * alpha\_W * (1 - l1\_ratio) * n\_features...scikit-learn.org/stable/modules/generated/sklearn.decomposition.NMF.html