xgboost
by base learnercv
method also returns the predictionmultilabel_evaluation
to also return the label measuresbrplus
because the newfeatures were using different levelsbaseline
using hamming-loss to prevent empty label predictionhomer
to deal with labels without intances and to predict instances
based on the meta-label scoresNew multi-label transformation methods including pairwise and multiclass approaches. Some fixes from previous version.
multilabel_confusion_matrix
accepts a data.frame or matrix with the predicitonsFirst release of utiml:
Binary Relevance (BR)
; BR+
; Classifier Chains
;
ConTRolled Label correlation exploitation (CTRL)
; Dependent Binary Relevance (DBR)
;
Ensemble of Binary Relevance (EBR)
; Ensemble of Classifier Chains (ECC)
;
Meta-Binary Relevance (MBR or 2BR)
; Nested Stacking (NS)
;
Pruned and Confident Stacking Approach (Prudent)
; and, Recursive Dependent Binary Relevance (RDBR)
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