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Journal Articles The Journal of Chemical Physics Year : 2020

Probabilistic performance estimators for computational chemistry methods: Systematic improvement probability and ranking probability matrix. I. Theory

Abstract

The comparison of benchmark error sets is an essential tool for the evaluation of theories in computational chemistry. The standard ranking of methods by their mean unsigned error is unsatisfactory for several reasons linked to the non-normality of the error distributions and the presence of underlying trends. Complementary statistics have recently been proposed to palliate such deficiencies, such as quantiles of the absolute error distribution or the mean prediction uncertainty. We introduce here a new score, the systematic improvement probability, based on the direct system-wise comparison of absolute errors. Independent of the chosen scoring rule, the uncertainty of the statistics due to the incompleteness of the benchmark datasets is also generally overlooked. However, this uncertainty is essential to appreciate the robustness of rankings. In the present article, we develop two indicators based on robust statistics to address this problem: Pinv, the inversion probability between two values of a statistic, and Pr, the ranking probability matrix. We demonstrate also the essential contribution of the correlations between error sets in these scores comparisons.
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hal-02563578 , version 1 (20-06-2024)

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Pascal Pernot, Andreas Savin. Probabilistic performance estimators for computational chemistry methods: Systematic improvement probability and ranking probability matrix. I. Theory. The Journal of Chemical Physics, 2020, 152 (16), pp.164108. ⟨10.1063/5.0006202⟩. ⟨hal-02563578⟩
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