Using the Gini coefficient to characterize the shape of computational chemistry error distributions
Author:
Publisher
Springer Science and Business Media LLC
Subject
Physical and Theoretical Chemistry
Link
http://link.springer.com/content/pdf/10.1007/s00214-021-02725-0.pdf
Reference44 articles.
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2. Pernot P, Savin A (2018) Probabilistic performance estimators for computational chemistry methods: the empirical cumulative distribution function of absolute errors. J Chem Phys 148:241707. https://doi.org/10.1063/1.5016248
3. Pernot P, Savin A (2020) Probabilistic performance estimators for computational chemistry methods: systematic improvement probability and ranking probability matrix. I. Theory J Chem Phys 152:164108. https://doi.org/10.1063/5.0006202
4. Pernot P, Savin A (2020) Probabilistic performance estimators for computational chemistry methods: Systematic improvement probability and ranking probability matrix. II. Appl J Chem Phys 152:164109. https://doi.org/10.1063/5.0006204
5. Pernot P, Huang B, Savin A (2020) Impact of non-normal error distributions on the benchmarking and ranking of Quantum Machine Learning models. Mach Learn Sci Technol 1:035011. https://doi.org/10.1088/2632-2153/aba184
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