E12-1035 guessing strategies . We now turn to macro-averaging across multiple classifiers or
C04-1137 in the test set . The choice of macro-averaging over micro-averaging does not
D13-1070 cision ) than summaries B . When macro-averaging , the Recall score of summaries
P11-2044 aligned token pairs per problem and macro-averaging over all problems . The results
P07-1097 distinction between micro - and macro-averaging often seen in classification
C02-1103 the micro-averaging method and macro-averaging method . 2.2 Experimental Results
P07-1097 distinction similar to micro - and macro-averaging in the context of classification
D11-1072 called micro-averaging , the latter macro-averaging . As we use a knowledge base
D12-1035 measures by both micro-averaging and macro-averaging . Micro-averaging aggregates
E12-1035 distribution of weights . Commonly macro-averaging averages across classes as average
C04-1070 then compute the F1 score . In macro-averaging , the F1 score is computed for
P07-1097 distinction between micro - and macro-averaging discussed in the context of text
P13-1141 precision/recall/f - measure . For macro-averaging , we compute a single confusion
I05-3015 categories . The way is called macro-averaging method . For evaluating performance
E12-1035 controversy over these averages , and macro-averaging in general , relates to one of
N12-2006 sentence in the treebank ( termed macro-averaging , in contrast to microaveraging
P05-1062 the basic evaluation metrics and macro-averaging strategy was used to calculate
D08-1114 row " average " was obtained by macro-averaging ( average of av - erages ) .
N12-1021 and Flach , 2011 ) . However , macro-averaging better reflects performance on
P13-1160 the least important , so we use macro-averaging over labels and then average
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