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MethodMapping — scikit-learn 1.6.1 documentation
Added in version 1.3. add ( * , caller , callee )...scikit-learn.org/stable/modules/generated/sklearn.utils.metadata_routing.MethodMapping.html -
MinCovDet — scikit-learn 1.6.1 documentation
algorithm: (n_samples + n_features + 1) / 2 * n_samples . The parameter...parameter must be in the range (0, 1]. random_state int, RandomState...scikit-learn.org/stable/modules/generated/sklearn.covariance.MinCovDet.html -
MetaEstimatorMixin — scikit-learn 1.6.1 documen...
Changed in version 1.6: The _required_parameters is...scikit-learn.org/stable/modules/generated/sklearn.base.MetaEstimatorMixin.html -
Release Highlights — scikit-learn 1.6.1 documen...
scikit-learn 1.1 Release Highlights for scikit-learn 1.1 Release Highlights...scikit-learn 1.6 Release Highlights for scikit-learn 1.6 Release...scikit-learn.org/stable/auto_examples/release_highlights/index.html -
Ability of Gaussian process regression (GPR) to...
reshape ( - 1 , 1 ) y = target_generator ( X ,..., size = 20 ) . reshape ( - 1 , 1 ) y_train = target_generator...scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_noisy.html -
Marvel Reference for 2.x and 1.x | Elastic
x and 1.x: 2.1 Marvel Reference for 2.x and 1.x: 2.0 Marvel...Reference for 2.x and 1.x Marvel Reference for 2.x and 1.x: 2.4 (current)...www.elastic.co/guide/en/marvel/index.html -
Multilabel classification — scikit-learn 1.6.1 ...
1 ]) max_y = np . max ( X [:, 1 ]) classif = OneVsRestClassifier...where ( Y [:, 1 ]) plt . scatter ( X [:, 0 ], X [:, 1 ], s = 40 ,...scikit-learn.org/stable/auto_examples/miscellaneous/plot_multilabel.html -
Quantile regression — scikit-learn 1.6.1 docume...
scikit-learn 1.1 Release Highlights for scikit-learn 1.1 Lagged features...axs [ 1 , 0 ] . set_xlabel ( "Residuals" ) _ = axs [ 1 , 1 ] ....scikit-learn.org/stable/auto_examples/linear_model/plot_quantile_regression.html -
is_multilabel — scikit-learn 1.6.1 documentation
1 , 0 , 1 ]) False >>> is_multilabel ([[ 1 ], [ 0 ,...is_multilabel ( np . array ([[ 1 , 0 ], [ 0 , 0 ]])) True >>> is_multilabel...scikit-learn.org/stable/modules/generated/sklearn.utils.multiclass.is_multilabel.html -
Getting Started — scikit-learn 1.6.1 documentation
dataset is easy array([1., 1., 1., 1., 1.]) Automatic parameter...transform ( X ) array([[-1., 1.], [ 1., -1.]]) Sometimes, you want...scikit-learn.org/stable/getting_started.html