Using the Gini coefficient to characterize the shape of computational chemistry error distributions

Pascal Pernot; Andreas Savin

doi:10.1007/s00214-021-02725-0

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arXiv · 2012.09589

Using the Gini coefficient to characterize the shape of computational chemistry error distributions

Pascal Pernot·Andreas Savin

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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.

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Pascal Pernot, Andreas Savin. 2020-12-17. Using the Gini coefficient to characterize the shape of computational chemistry error distributions. https://doi.org/10.1007/s00214-021-02725-0

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