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brier_score_loss — scikit-learn 1.8.0 doc...
y_true in {-1, 1} or {0, 1}, pos_label defaults to 1; else if y_true...defined as: \[\frac{1}{N}\sum_{i=1}^{N}\sum_{c=1}^{C}(y_{ic} - \hat{p}_{ic})^{2}\]...scikit-learn.org/stable/modules/generated/sklearn.metrics.brier_score_loss.html -
cluster_optics_xi — scikit-learn 1.8.0 do...
1, 1, 1]) >>> clusters array([[0,...min_samples int > 1 or float between 0 and 1 The same as the min_samples...scikit-learn.org/stable/modules/generated/sklearn.cluster.cluster_optics_xi.html -
sparse_encode — scikit-learn 1.8.0 docume...
1 , 0 ], ... [ - 1 , - 1 , 2 ], ... [ 1 , 1 , 1 ], ......>>> X = np . array ([[ - 1 , - 1 , - 1 ], [ 0 , 0 , 3 ]]) >>>...scikit-learn.org/stable/modules/generated/sklearn.decomposition.sparse_encode.html -
completeness_score — scikit-learn 1.8.0 d...
1 , 1 ], [ 1 , 1 , 0 , 0 ]) 1.0 Non-perfect labelings...completeness_score ([ 0 , 0 , 1 , 1 ], [ 0 , 1 , 0 , 1 ])) 0.0 >>>...scikit-learn.org/stable/modules/generated/sklearn.metrics.completeness_score.html -
SparseCoder — scikit-learn 1.8.0 document...
1 , 0 ], ... [ - 1 , - 1 , 2 ], ... [ 1 , 1 , 1 ], ......>>> X = np . array ([[ - 1 , - 1 , - 1 ], [ 0 , 0 , 3 ]]) >>>...scikit-learn.org/stable/modules/generated/sklearn.decomposition.SparseCoder.html -
VotingClassifier — scikit-learn 1.8.0 doc...
([[ - 1 , - 1 ], [ - 2 , - 1 ], [ - 3 , - 2 ], [ 1 , 1 ], [ 2...2 , 1 ], [ 3 , 2 ]]) >>> y = np . array ([ 1 , 1 , 1...scikit-learn.org/stable/modules/generated/sklearn.ensemble.VotingClassifier.html -
PredefinedSplit — scikit-learn 1.8.0 docu...
1 , 1 ]) >>> test_fold = [ 0 , 1 , - 1 , 1 ] >>>...PredefinedSplit(test_fold=array([ 0, 1, -1, 1])) >>> for i , (...scikit-learn.org/stable/modules/generated/sklearn.model_selection.PredefinedSplit.html -
f1_score — scikit-learn 1.8.0 documentation
[ 1 , 1 , 1 ], [ 0 , 1 , 1 ]] >>> y_pred...= [[ 0 , 0 , 0 ], [ 1 , 1 , 1 ], [ 1 , 1 , 0 ]] >>>...scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html -
type_of_target — scikit-learn 1.8.0 docum...
>>> type_of_target ([ 1 , - 1 , - 1 , 1 ]) 'binary' >>>...type_of_target ( np . array ([[ 0 , 1 ], [ 1 , 1 ]])) 'multilabel-indicator'...scikit-learn.org/stable/modules/generated/sklearn.utils.multiclass.type_of_target.html -
LabelEncoder — scikit-learn 1.8.0 documen...
array([1, 2, 6]) >>> le . transform ([ 1 , 1 , 2 , 6...inverse_transform ([ 0 , 0 , 1 , 2 ]) array([1, 1, 2, 6]) It can also be used...scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html