Feature Selection in Multivariate Linear Regression Models for Data-Efficient Reaction Optimization

Abstract

Abstract We introduce NaviCat ReaFS (reaction feature selection), a framework for testing and supporting multivariate linear regression models for reaction optimization in small, heterogeneous datasets, where overfitting risk is high and additional experiments are costly. ReaFS combines systematic benchmarking of feature selection methods with targeted diagnostics spanning data, feature-subsets, and predictions. We also introduce a composite score that captures complementary aspects of extrapolative performance, namely, candidate recovery, predictive accuracy, and ranking quality, enabling direct comparison of models and feature selection strategies across homogeneous catalysis datasets. ReaFS further integrates cross-validation schemes, data and feature diagnostics, and robustness checks for prospective model assessment. Together, these components provide a practical route to more informed decision-making in low-data reaction optimization.