Using the Gini coefficient to characterize the shape of computational chemistry error distributions
Chemical Physics
2021-02-19 v1 Data Analysis, Statistics and Probability
Abstract
The distribution of errors is a central object in the assesment and benchmarking of computational chemistry methods. The popular and often blind use of the mean unsigned error as a benchmarking statistic leads to ignore distributions features that impact the reliability of the tested methods. We explore how the Gini coefficient offers a global representation of the errors distribution, but, except for extreme values, does not enable an unambiguous diagnostic. We propose to relieve the ambiguity by applying the Gini coefficient to mode-centered error distributions. This version can usefully complement benchmarking statistics and alert on error sets with potentially problematic shapes.
Keywords
Cite
@article{arxiv.2012.09589,
title = {Using the Gini coefficient to characterize the shape of computational chemistry error distributions},
author = {Pascal Pernot and Andreas Savin},
journal= {arXiv preprint arXiv:2012.09589},
year = {2021}
}