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GroupKFold — scikit-learn 1.7.2 documentation
, 10 ], [ 11 , 12 ]]) >>> y = np . array ([ 1 , 2 , 3 , 4 , 5...5 , 6 ]) >>> groups = np . array ([ 0 , 0 , 2 , 2 , 3 , 3 ]) >>>...scikit-learn.org/stable/modules/generated/sklearn.model_selection.GroupKFold.html -
ShuffleSplit — scikit-learn 1.7.2 documentation
split ( X )): ... print ( f "Fold { i } :" ) ... print ( f " Train:...enumerate ( rs . split ( X )): ... print ( f "Fold { i } :" ) ... print...scikit-learn.org/stable/modules/generated/sklearn.model_selection.ShuffleSplit.html -
RepeatedStratifiedKFold — scikit-learn 1.7.2 do...
np . array ([[ 1 , 2 ], [ 3 , 4 ], [ 1 , 2 ], [ 3 , 4 ]]) >>>...enumerate ( rskf . split ( X , y )): ... print ( f "Fold { i } :" ) ......scikit-learn.org/stable/modules/generated/sklearn.model_selection.RepeatedStratifiedKFold.html -
pairwise_distances — scikit-learn 1.7.2 documen...
[‘cityblock’, ‘cosine’, ‘euclidean’, ‘l1’, ‘l2’, ‘manhattan’, ‘nan_euclidean’]....‘canberra’, ‘chebyshev’, ‘correlation’, ‘dice’, ‘hamming’, ‘jaccard’,...scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise_distances.html -
LocalOutlierFactor — scikit-learn 1.7.2 documen...
Machine. References [ 1 ] Breunig, M. M., Kriegel, H. P., Ng, R....‘ball_tree’, ‘kd_tree’, ‘brute’}, default=’auto’ Algorithm used to compute...scikit-learn.org/stable/modules/generated/sklearn.neighbors.LocalOutlierFactor.html -
OrdinalEncoder — scikit-learn 1.7.2 documentation
([[ 1 , 0 ], [ 0 , 1 ]]) array([['Male', 1], ['Female', 2]], dtype=object)...]]) array([[0., 2.], [1., 0.]]) >>> enc . inverse_transform ([[...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OrdinalEncoder.html -
LearningCurveDisplay — scikit-learn 1.7.2 docum...
train_sizes = array([0.1, 0.33, 0.55, 0.78, 1.]) , cv = None , scoring =...(n_ticks,), default=np.linspace(0.1, 1.0, 5) Relative or absolute...scikit-learn.org/stable/modules/generated/sklearn.model_selection.LearningCurveDisplay.html -
Covariance estimation — scikit-learn 1.7.2 docu...
concerning the sklearn.covariance module. Ledoit-Wolf vs OAS estimation...Ctrl + K GitHub Choose version Covariance estimation # Examples...scikit-learn.org/stable/auto_examples/covariance/index.html -
StandardScaler — scikit-learn 1.7.2 documentation
transform ( data )) [[-1. -1.] [-1. -1.] [ 1. 1.] [ 1. 1.]] >>> print...>>> data = [[ 0 , 0 ], [ 0 , 0 ], [ 1 , 1 ], [ 1 , 1 ]] >>> scaler...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html -
cross_validate — scikit-learn 1.7.2 documentation
instead. E.g.: cross_validate(..., params={'groups': groups}) . scoring...instance (e.g., GroupKFold ). Changed in version 1.4: groups can...scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_validate.html