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Interpretable Meta-Measure for Model Performance | | Alicja Gosiewska
; Katarzyna Woznica
; Przemyslaw Biecek
; | | Date: |
2 Jun 2020 | | Abstract: | Measures for evaluation of model performance play an important role in
Machine Learning. However, the most common performance measures share several
limitations. The difference in performance for two models has no probabilistic
interpretation and there is no reference point to indicate whether they
represent a significant improvement. What is more, it makes no sense to compare
such differences between data sets. In this article, we introduce a new
meta-measure for performance assessment named Elo-based Predictive Power (EPP).
The differences in EPP scores have probabilistic interpretation and can be
directly compared between data sets. We prove the mathematical properties of
EPP and support them with empirical results of a~large scale benchmark on 30
classification data sets. Finally, we show applications of EPP to the selected
meta-learning problems and challenges beyond ML benchmarks. | | Source: | arXiv, 2006.2293 | | Services: | Forum | Review | PDF | Favorites | |
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