Paired evaluation of machine-learning models characterizes effects of confounders and outliers

Maulik K Nariya; Caitlin E Mills; Peter K Sorger; Artem Sokolov

doi:10.1016/j.patter.2023.100791

Paired evaluation of machine-learning models characterizes effects of confounders and outliers

Patterns (N Y). 2023 Jul 7;4(8):100791. doi: 10.1016/j.patter.2023.100791. eCollection 2023 Aug 11.

Authors

Maulik K Nariya^{1

2}, Caitlin E Mills¹, Peter K Sorger^{1

2}, Artem Sokolov^{1

3}

Affiliations

¹ Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA 02115, USA.
² Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA.
³ Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.

Abstract

The true accuracy of a machine-learning model is a population-level statistic that cannot be observed directly. In practice, predictor performance is estimated against one or more test datasets, and the accuracy of this estimate strongly depends on how well the test sets represent all possible unseen datasets. Here we describe paired evaluation as a simple, robust approach for evaluating performance of machine-learning models in small-sample biological and clinical studies. We use the method to evaluate predictors of drug response in breast cancer cell lines and of disease severity in patients with Alzheimer's disease, demonstrating that the choice of test data can cause estimates of performance to vary by as much as 20%. We show that paired evaluation makes it possible to identify outliers, improve the accuracy of performance estimates in the presence of known confounders, and assign statistical significance when comparing machine-learning models.

Keywords: Confounding Variables; Machine Learning; Model evaluation; Outlier Detection; Small-sample studies.