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lars_path_gram — scikit-learn 1.8.0 documentation
the case method=’lasso’ is: ( 1 / ( 2 * n_samples )) * || y -...equation (see discussion in [1] ). Read more in the User Guide...scikit-learn.org/stable/modules/generated/sklearn.linear_model.lars_path_gram.html -
radius_neighbors_graph — scikit-learn 1.8.0 doc...
() array([[1., 0., 1.], [0., 1., 0.], [1., 0., 1.]]) On this...means 1 unless in a joblib.parallel_backend context. -1 means...scikit-learn.org/stable/modules/generated/sklearn.neighbors.radius_neighbors_graph.html -
FeatureUnion — scikit-learn 1.8.0 documentation
svd__n_components = 1 ) . fit_transform ( X ) array([[-1.5 , 3.04], [ 1.5 , 5.72]])...unchanged. Added in version 1.1: Added the option "passthrough"...scikit-learn.org/stable/modules/generated/sklearn.pipeline.FeatureUnion.html -
make_swiss_roll — scikit-learn 1.8.0 documentation
is from Marsland [1] . References [ 1 ] ( 1 , 2 ) S. Marsland,...from Stephen Marsland’s code [1] . Parameters : n_samples int,...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_swiss_roll.html -
LabelSpreading — scikit-learn 1.8.0 documentation
means 1 unless in a joblib.parallel_backend context. -1 means...Clamping factor. A value in (0, 1) that specifies the relative amount...scikit-learn.org/stable/modules/generated/sklearn.semi_supervised.LabelSpreading.html -
shuffle — scikit-learn 1.8.0 documentation
1.], [1., 0.]]) >>> y array([2, 1, 0]) >>> shuffle...>>> X = np . array ([[ 1. , 0. ], [ 2. , 1. ], [ 0. , 0. ]]) >>>...scikit-learn.org/stable/modules/generated/sklearn.utils.shuffle.html -
PassiveAggressiveClassifier — scikit-learn 1.8....
deprecated in version 1.8 and will be removed in 1.10. Instead use:...means 1 unless in a joblib.parallel_backend context. -1 means...scikit-learn.org/stable/modules/generated/sklearn.linear_model.PassiveAggressiveClassifier.html -
MultiTaskElasticNet — scikit-learn 1.8.0 docume...
1 ) >>> clf . fit ([[ 0 , 0 ], [ 1 , 1 ], [ 2 , 2...], [ 1 , 1 ], [ 2 , 2 ]]) MultiTaskElasticNet(alpha=0.1) >>>...scikit-learn.org/stable/modules/generated/sklearn.linear_model.MultiTaskElasticNet.html -
zero_one_loss — scikit-learn 1.8.0 documentation
1 ], [ 1 , 1 ]]), np . ones (( 2 , 2...zero_one_loss >>> y_pred = [ 1 , 2 , 3 , 4 ] >>> y_true = [ 2...scikit-learn.org/stable/modules/generated/sklearn.metrics.zero_one_loss.html -
SVR — scikit-learn 1.8.0 documentation
Added in version 1.1. n_support_ ndarray of shape (1,), dtype=int32..., tol = 0.001 , C = 1.0 , epsilon = 0.1 , shrinking = True ,...scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html