LCEN: A Novel Feature Selection Algorithm for Nonlinear, Interpretable Machine Learning Models

arXiv:2402.17120v1 Announce Type: new
Abstract: Interpretable architectures can have advantages over black-box architectures, and interpretability is essential for the application of machine learning in critical settings, such as aviation or medicine. However, the simplest, most commonly used interpretable architectures (such as LASSO or EN) are limited to linear predictions and have poor feature selection capabilities. In this work, we introduce the LASSO-Clip-EN (LCEN) algorithm for the creation of nonlinear, interpretable machine learning models. LCEN is tested on a wide variety …

abstract advantages algorithm application architectures arxiv aviation box cs.lg feature feature selection interpretability lasso linear machine machine learning machine learning models medicine novel type