Why only Micro-F1? Class Weighting of Measures for Relation Classification
Computation and Language
2022-05-20 v1 Machine Learning
Abstract
Relation classification models are conventionally evaluated using only a single measure, e.g., micro-F1, macro-F1 or AUC. In this work, we analyze weighting schemes, such as micro and macro, for imbalanced datasets. We introduce a framework for weighting schemes, where existing schemes are extremes, and two new intermediate schemes. We show that reporting results of different weighting schemes better highlights strengths and weaknesses of a model.
Keywords
Cite
@article{arxiv.2205.09460,
title = {Why only Micro-F1? Class Weighting of Measures for Relation Classification},
author = {David Harbecke and Yuxuan Chen and Leonhard Hennig and Christoph Alt},
journal= {arXiv preprint arXiv:2205.09460},
year = {2022}
}
Comments
NLP Power! The First Workshop on Efficient Benchmarking in NLP (ACL 2022)