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SelectKBest — scikit-learn 1.8.0 documentation
scikit-learn 1.1 Release Highlights for scikit-learn 1.1 On this page...Added in version 0.18. k int or “all”, default=10 Number of top...scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectKBest.html -
partial_dependence — scikit-learn 1.8.0 documen...
>>> X = [[ 0 , 0 , 2 ], [ 1 , 0 , 0 ]] >>> y = [ 0 , 1 ] >>> from...percentiles = (0.05, 0.95) , grid_resolution = 100 , custom_values...scikit-learn.org/stable/modules/generated/sklearn.inspection.partial_dependence.html -
non_negative_factorization — scikit-learn 1.8.0...
([[ 1 , 1 ], [ 2 , 1 ], [ 3 , 1.2 ], [ 4 , 1 ], [ 5 , 0.8 ],...'frobenius' , tol = 0.0001 , max_iter = 200 , alpha_W = 0.0 , alpha_H =...scikit-learn.org/stable/modules/generated/sklearn.decomposition.non_negative_factorization.html -
SelectFromModel — scikit-learn 1.8.0 documentation
= [[ 0.87 , - 1.34 , 0.31 ], ... [ - 2.79 , - 0.02 , - 0.85 ],...... [ - 1.34 , - 0.48 , - 2.55 ], ... [ 1.92 , 1.48 , 0.65 ]] >>>...scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectFromModel.html -
TfidfVectorizer — scikit-learn 1.8.0 documentation
ngram_range=(1 , 1) , max_df=1.0 , min_df=1 , max_features=None...words). If float in range [0.0, 1.0], the parameter represents...scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html -
GenericUnivariateSelect — scikit-learn 1.8.0 do...
Added in version 1.0. See also f_classif ANOVA F-value...mode='percentile' , param=1e-05 ) [source] # Univariate feature...scikit-learn.org/stable/modules/generated/sklearn.feature_selection.GenericUnivariateSelect.html -
get_scorer — scikit-learn 1.8.0 documentation
reshape ([ 0 , 1 , - 1 , - 0.5 , 2 ], ( - 1 , 1 )) >>> y = np...np . array ([ 0 , 1 , 1 , 0 , 1 ]) >>> classifier = DummyClassifier...scikit-learn.org/stable/modules/generated/sklearn.metrics.get_scorer.html -
precision_recall_curve — scikit-learn 1.8.0 doc...
1. , 0.5, 0.5, 0. ]) >>> thresholds array([0.1 , 0.35, 0.4 ,...([ 0 , 0 , 1 , 1 ]) >>> y_scores = np . array ([ 0.1 , 0.4 ,...scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_curve.html -
precision_recall_fscore_support — scikit-learn ...
(array([0. , 0. , 0.66]), array([0., 0., 1.]), array([0. , 0. , 0.8]),...weights. zero_division {“warn”, 0.0, 1.0, np.nan}, default=”warn” Sets...scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_fscore_support.html -
MissingIndicator — scikit-learn 1.8.0 documenta...
1 , 3 ], ... [ 4 , 0 , np . nan ], ... [ 8 , 1 , 0 ]]) >>>...all strings. Added in version 1.0. See also SimpleImputer Univariate...scikit-learn.org/stable/modules/generated/sklearn.impute.MissingIndicator.html