fabOF: A Novel Tree Ensemble Method for Ordinal Prediction

Ordinal responses commonly occur in the life sciences, e.g., through school grades or rating scales. Where traditionally parametric statistical models have been used, machine learning (ML) methods such as random forest (RF) are increasingly employed for ordinal prediction. As RF does not account for ordinality, several extensions have been proposed. A promising approach lies in assigning optimized numeric scores to the ordinal response categories and using regression RF. However, these optimization procedures are computationally expensive and have been shown to yield only situational benefit. In this work, I propose Frequency Adjusted Borders Ordinal Forest (fabOF), a novel tree ensemble method for ordinal prediction forgoing extensive optimization while offering improved predictive performance in simulation and an illustrative example of student performance.