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Comparison of Calibration of Classifiers —...
1 ) proba_neg_class = 1 - proba_pos_class proba...[( 2 , 0 ), ( 2 , 1 ), ( 3 , 0 ), ( 3 , 1 )] for i , ( _ , name...scikit-learn.org/stable/auto_examples/calibration/plot_compare_calibration.html -
chi2 — scikit-learn 1.8.0 documentation
array ([[ 1 , 1 , 3 ], ... [ 0 , 1 , 5 ], ... [ 5 , 4 , 1 ], ......>>> y = np . array ([ 1 , 1 , 0 , 0 , 2 , 2 ]) >>>...scikit-learn.org/stable/modules/generated/sklearn.feature_selection.chi2.html -
LeavePOut — scikit-learn 1.8.0 documentation
3] Test: index=[0 1] Fold 1: Train: index=[1 3] Test: index=[0...index=[1 2] Fold 4: Train: index=[0 2] Test: index=[1 3] Fold...scikit-learn.org/stable/modules/generated/sklearn.model_selection.LeavePOut.html -
RepeatedKFold — scikit-learn 1.8.0 docume...
array ([[ 1 , 2 ], [ 3 , 4 ], [ 1 , 2 ], [ 3 , 4 ]])...>>> y = np . array ([ 0 , 0 , 1 , 1 ]) >>> rkf = RepeatedKFold...scikit-learn.org/stable/modules/generated/sklearn.model_selection.RepeatedKFold.html -
PolynomialFeatures — scikit-learn 1.8.0 d...
fit_transform ( X ) array([[ 1., 0., 1., 0., 0., 1.], [ 1., 2., 3., 4., 6.,...) array([[ 1., 0., 1., 0.], [ 1., 2., 3., 6.], [ 1., 4., 5., 20.]])...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html -
SGD: Weighted samples — scikit-learn 1.8....
) + [ 1 , 1 ], np . random . randn ( 10 , 2 )] y = [ 1 ] * 10...10 + [ - 1 ] * 10 sample_weight = 100 * np . abs ( np . random...scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_weighted_samples.html -
cosine_similarity — scikit-learn 1.8.0 do...
[ 1 , 1 , 1 ]] >>> Y = [[ 1 , 0 , 0 ], [ 1 , 1 , 0...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.cosine_similarity.html -
RadiusNeighborsClassifier — scikit-learn ...
() array([[1., 0., 1.], [0., 1., 0.], [1., 0., 1.]]) score (...], [ 1 ], [ 2 ], [ 3 ]] >>> y = [ 0 , 0 , 1 , 1 ] >>>...scikit-learn.org/stable/modules/generated/sklearn.neighbors.RadiusNeighborsClassifier.html -
MaxAbsScaler — scikit-learn 1.8.0 documen...
-1. , 1. ], [ 1. , 0. , 0. ], [ 0. , 1. , -0.5]]) fit...= [[ 1. , - 1. , 2. ], ... [ 2. , 0. , 0. ], ... [ 0. , 1. , -...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MaxAbsScaler.html -
fbeta_score — scikit-learn 1.8.0 document...
1 , 2 , 0 , 1 , 2 ] >>> y_pred = [ 0 , 2 , 1 , 0...beta > 1 gives more weight to recall, while beta < 1 favors...scikit-learn.org/stable/modules/generated/sklearn.metrics.fbeta_score.html