Variation in Predictive Performance across Treatments of Ordinal Outcomes in Machine Learning

The present study investigates the performance of an ordinal regression approach for machine learning in new conditions, algorithms, and performance metrics beyond what has previously been evaluated. Findings reveal the need for careful and deliberate choices in the treatment of ordinal outcome variables in machine learning for optimal predictive performance.