%0 Generic %1 opitz2019macro %A Opitz, Juri %A Burst, Sebastian %D 2019 %K f1 machinelearning %T Macro F1 and Macro F1 %U http://arxiv.org/abs/1911.03347 %X The 'macro F1' metric is frequently used to evaluate binary, multi-class and multi-label classification problems. Yet, we find that there exist two different formulas to calculate this quantity. In this note, we show that only under rare circumstances, the two computations can be considered equivalent. More specifically, one formula well 'rewards' classifiers which produce a skewed error type distribution. In fact, the difference in outcome of the two computations can be as high as 0.5. Finally, we show that the two computations may not only diverge in their scalar result but also lead to different classifier rankings.

@misc{opitz2019macro, abstract = {The 'macro F1' metric is frequently used to evaluate binary, multi-class and multi-label classification problems. Yet, we find that there exist two different formulas to calculate this quantity. In this note, we show that only under rare circumstances, the two computations can be considered equivalent. More specifically, one formula well 'rewards' classifiers which produce a skewed error type distribution. In fact, the difference in outcome of the two computations can be as high as 0.5. Finally, we show that the two computations may not only diverge in their scalar result but also lead to different classifier rankings.}, added-at = {2019-11-11T17:22:52.000+0100}, author = {Opitz, Juri and Burst, Sebastian}, biburl = {https://www.bibsonomy.org/bibtex/28c15bdd17bf253ebbe5e70264975f0fe/cpankow}, description = {[1911.03347] Macro F1 and Macro F1}, interhash = {6dd2dc9f3d6adb186b532f91550bc155}, intrahash = {8c15bdd17bf253ebbe5e70264975f0fe}, keywords = {f1 machinelearning}, note = {cite arxiv:1911.03347}, timestamp = {2019-11-11T17:22:52.000+0100}, title = {Macro F1 and Macro F1}, url = {http://arxiv.org/abs/1911.03347}, year = 2019 }