ACS Catalysis· 2026Q1
Feature Selection in Multivariate Linear Regression Models for Data-Efficient Reaction Optimization
- 1citations
- Q1SCImago
- 2026year
Short summary
NaviCat ReaFS, a new framework, systematically benchmarks feature selection methods for multivariate linear regression in low-data reaction optimization, reducing overfitting risk and experiment costs.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduces NaviCat ReaFS, a framework for feature selection in multivariate linear regression for reaction optimization.
- Addresses challenges of small, heterogeneous datasets where overfitting is high and experiments are costly.
- Combines systematic benchmarking of feature selection methods with targeted diagnostics.
- Features a composite score to assess extrapolative performance (candidate recovery, predictive accuracy, ranking quality).
- Integrates cross-validation, data/feature diagnostics, and robustness checks for prospective model assessment.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. ACS Catalysis, 2026 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
Field: Materials Chemistry
Materials ChemistryMaterials Science