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LogisticRegressionCV — scikit-learn 1.8.0...
l1_ratio = 1 it is an L1 penalty. For 0 < l1_ratio < 1 , the...since version 1.8: l1_ratios=None is deprecated in 1.8 and will...scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegressionCV.html -
KBinsDiscretizer — scikit-learn 1.8.0 doc...
[ 1., 1., 1., 0.], [ 2., 2., 2., 1.], [ 2., 2., 2.,...>>> X = [[ - 2 , 1 , - 4 , - 1 ], ... [ - 1 , 2 , - 3 , - 0.5...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.KBinsDiscretizer.html -
NuSVR — scikit-learn 1.8.0 documentation
Added in version 1.1. n_support_ ndarray of shape (1,), dtype=int32...(0, 1]. By default 0.5 will be taken. C float, default=1.0 Penalty...scikit-learn.org/stable/modules/generated/sklearn.svm.NuSVR.html -
Multiclass Receiver Operating Characteristic (R...
1 ], [ 0 , 1 ], "k--" , label...xlim = ( - 0.01 , 1.01 ), ylim = ( - 0.01 , 1.01 ), ) We confirm...scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html -
SpectralBiclustering — scikit-learn 1.8.0...
array ([[ 1 , 1 ], [ 2 , 1 ], [ 1 , 0 ], ... [ 4 , 7...clustering . row_labels_ array([1, 1, 1, 0, 0, 0], dtype=int32) >>>...scikit-learn.org/stable/modules/generated/sklearn.cluster.SpectralBiclustering.html -
AffinityPropagation — scikit-learn 1.8.0 ...
array ([[ 1 , 2 ], [ 1 , 4 ], [ 1 , 0 ], ... [ 4 , 2.... labels_ array([0, 0, 0, 1, 1, 1]) >>> clustering ....scikit-learn.org/stable/modules/generated/sklearn.cluster.AffinityPropagation.html -
LeaveOneOut — scikit-learn 1.8.0 document...
([[ 1 , 2 ], [ 3 , 4 ]]) >>> y = np . array ([ 1 , 2...0: Train: index=[1] Test: index=[0] Fold 1: Train: index=[0]...scikit-learn.org/stable/modules/generated/sklearn.model_selection.LeaveOneOut.html -
homogeneity_completeness_v_measure — scik...
1 , 1 , 2 , 2 ], [ 0 , 0 , 1 , 2 , 2 , 2 ] >>>...float Score between 0.0 and 1.0. 1.0 stands for perfectly homogeneous...scikit-learn.org/stable/modules/generated/sklearn.metrics.homogeneity_completeness_v_measure.html -
inplace_csr_row_normalize_l1 — scikit-lea...
1 , 2 , 3 ]) >>> data = np . array ([ 1.0 , 2.0...0. ], [0. , 0. , 1. , 0. ], [0. , 0. , 0. , 1. ]]) On this page...scikit-learn.org/stable/modules/generated/sklearn.utils.sparsefuncs_fast.inplace_csr_row_normaliz... -
Demonstrating the different strategies of KBins...
strategies ) + 1 , i ) ax . scatter ( X [:, 0 ], X [:, 1 ], edgecolors.... linspace ( X [:, 1 ] . min (), X [:, 1 ] . max (), 300 ), )...scikit-learn.org/stable/auto_examples/preprocessing/plot_discretization_strategies.html