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

• URL: http://arxiv.org/abs/2010.00110v2
• Date: Fri, 22 Jan 2021 22:24:00 GMT
• Title: Metrics for Benchmarking and Uncertainty Quantification: Quality, Applicability, and a Path to Best Practices for Machine Learning in Chemistry
• Authors: Gaurav Vishwakarma, Aditya Sonpal, Johannes Hachmann
• Abstract summary: This review aims to draw attention to two issues of concern when we set out to make machine learning benchmarking work in the chemical and materials domain. They are often overlooked or underappreciated topics as chemists typically only have limited training in statistics. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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