# Metrics for Benchmarking and Uncertainty Quantification: Quality, Applicability, and a Path to Best Practices for Machine Learning in Chemistry

arXiv: https://arxiv.org/abs/2010.00110 · HTML: https://mlsanity.com/paper/2010.00110/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2020.

## Built on by (most cited first, 9 of 9)

- [Calibration in Machine Learning Uncertainty Quantification: beyond consistency to target adaptivity](https://mlsanity.com/paper/2309.06240/) (2023)
- [PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty](https://mlsanity.com/paper/2202.05063/) (2022)
- [A view on model misspecification in uncertainty quantification](https://mlsanity.com/paper/2210.16938/) (2022)
- [On minimizing the training set fill distance in machine learning regression](https://mlsanity.com/paper/2307.10988/) (2023)
- [Learning signals defined on graphs with optimal transport and Gaussian process regression](https://mlsanity.com/paper/2410.15721/) (2024)
- [Building Robust Machine Learning Models for Small Chemical Science Data: The Case of Shear Viscosity](https://mlsanity.com/paper/2208.10784/) (2022)
- [Bayesian Counterfactual Mean Embeddings and Off-Policy Evaluation](https://mlsanity.com/paper/2211.01518/) (2022)
- [Best practices for machine learning in antibody discovery and development](https://mlsanity.com/paper/2312.08470/) (2023)
- [On Fixing the Right Problems in Predictive Analytics: AUC Is Not the Problem](https://mlsanity.com/paper/2404.06989/) (2024)

## Abstract (arXiv)

This review aims to draw attention to two issues of concern when we set out to make machine learning work in the chemical and materials domain, i.e., statistical loss function metrics for the validation and benchmarking of data-derived models, and the uncertainty quantification of predictions made by them. They are often overlooked or underappreciated topics as chemists typically only have limited training in statistics. Aside from helping to assess the quality, reliability, and applicability of a given model, these metrics are also key to comparing the performance of different models and thus for developing guidelines and best practices for the successful application of machine learning in chemistry.