Machine learning workflows beyond linear models in low-data regimes.

ABSTRACT: Data-driven methodologies are transforming chemical research by providing chemists with digital tools that accelerate discovery and promote sustainability. In this context, non-linear machine learning algorithms are among the most disruptive technologies in the field and have proven effective for handling large datasets. However, in data-limited scenarios, linear regression has traditionally prevailed due to its simplicity and robustness, while non-linear models have been met with skepticism over concerns related to interpretability and overfitting. In this study, we introduce ready-to-use, automated workflows designed to overcome these challenges. These frameworks mitigate overfitting through Bayesian hyperparameter optimization by incorporating an objective function that accounts for ove

SUBMITTER: Dalmau D 

PROVIDER: S-EPMC11997861 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature