Jordan E. Loeffelman

Comparing the use of supervised machine learning variable selection methods in the context of two-group classification in the psychological and health sciences

Introduction Variable selection (VS) is crucial for building accurate and generalizable classification models. Reducing the necessary number of variables improves model efficiency, interpretability, and generalizability while reducing data collection burden. Despite the availability of various VS methods, their compara...