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.

References in corpus (7)

• Reliable estimation of prediction uncertainty for physico-chemical property models
• Choosing a density functional for static molecular polarizabilities
• Probabilistic performance estimators for computational chemistry methods: the empirical cumulative distribution function of absolute errors
• Impact of non-normal error distributions on the benchmarking and ranking of Quantum Machine Learning models
• Probabilistic performance estimators for computational chemistry methods: Systematic Improvement Probability and Ranking Probability Matrix. I. Theory
• The Gini Coefficient as a Morphological Measurement of Strongly Lensed Galaxies in the Image Plane
• Probabilistic performance estimators for computational chemistry methods: Systematic Improvement Probability and Ranking Probability Matrix. II. Applications

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