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CategoricalNB — scikit-learn 1.8.0 documentation
Added in version 1.2. Changed in version 1.4: The default value... CategoricalNB ( * , alpha = 1.0 , force_alpha = True , fit_prior...scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.CategoricalNB.html -
k_means — scikit-learn 1.8.0 documentation
[ 1., 2.]]) >>> label array([1, 1, 1, 0, 0, 0], dtype=int32)...X = np . array ([[ 1 , 2 ], [ 1 , 4 ], [ 1 , 0 ], ... [ 10 , 2...scikit-learn.org/stable/modules/generated/sklearn.cluster.k_means.html -
TheilSenRegressor — scikit-learn 1.8.0 document...
means 1 unless in a joblib.parallel_backend context. -1 means...the number of features (plus 1 if fit_intercept=True) and the...scikit-learn.org/stable/modules/generated/sklearn.linear_model.TheilSenRegressor.html -
Normalizer — scikit-learn 1.8.0 documentation
1 , 2 , 2 ], ... [ 1 , 3 , 9 , 3 ], ... [...0.4, 0.4], [0.1, 0.3, 0.9, 0.3], [0.5, 0.7, 0.5, 0.1]]) fit ( X...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Normalizer.html -
adjusted_rand_score — scikit-learn 1.8.0 docume...
1 , 1 ], [ 0 , 0 , 1 , 1 ]) 1.0 >>> adjusted_rand_score...adjusted_rand_score ([ 0 , 0 , 1 , 1 ], [ 1 , 1 , 0 , 0 ]) 1.0 Labelings that...scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_rand_score.html -
MultiOutputClassifier — scikit-learn 1.8.0 docu...
( X [ - 2 :]) array([[1, 1, 1], [1, 0, 1]]) fit ( X , Y , sample_weight...means 1 unless in a joblib.parallel_backend context. -1 means...scikit-learn.org/stable/modules/generated/sklearn.multioutput.MultiOutputClassifier.html -
StratifiedKFold — scikit-learn 1.8.0 documentation
array ([[ 1 , 2 ], [ 3 , 4 ], [ 1 , 2 ], [ 3 , 4 ]])...>>> y = np . array ([ 0 , 0 , 1 , 1 ]) >>> skf = StratifiedKFold...scikit-learn.org/stable/modules/generated/sklearn.model_selection.StratifiedKFold.html -
partial_dependence — scikit-learn 1.8.0 documen...
[ 1 , 0 , 0 ]] >>> y = [ 0 , 1 ] >>> from sklearn.ensemble...interacting features (e.g. [(0, 1)] ) for which the partial dependency...scikit-learn.org/stable/modules/generated/sklearn.inspection.partial_dependence.html -
nan_euclidean_distances — scikit-learn 1.8.0 do...
6] and [1, na, 4, 5] is: \[\sqrt{\frac{4}{2}((3-1)^2 + (6-5)^2)}\]...float ( "NaN" ) >>> X = [[ 0 , 1 ], [ 1 , nan ]] >>> nan_euclidean_distances...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.nan_euclidean_distances.html -
IsolationForest — scikit-learn 1.8.0 documentation
1 ], [ 0 ], [ 90 ]]) array([ 1, 1, -1]) For an example...from 0.1 to 'auto' . max_features int or float, default=1.0 The...scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html