Using the Gini coefficient to characterize the shape of computational chemistry error distributions - Sorbonne Université
Journal Articles Theoretical Chemistry Accounts: Theory, Computation, and Modeling Year : 2021

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

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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Dates and versions

hal-03148583 , version 1 (22-02-2021)

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Pascal Pernot, Andreas Savin. Using the Gini coefficient to characterize the shape of computational chemistry error distributions. Theoretical Chemistry Accounts: Theory, Computation, and Modeling, 2021, 140 (3), pp.24. ⟨10.1007/s00214-021-02725-0⟩. ⟨hal-03148583⟩
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