Frequency-adjusted borders ordinal forest: A novel tree ensemble method for ordinal prediction.

ABSTRACT: Ordinal responses commonly occur in psychology, e.g., through school grades or rating scales. Where traditionally parametric statistical models like the proportional odds model have been used, machine learning (ML) methods such as random forest (RF) are increasingly employed for ordinal prediction. With new developments in assessment and new data sources yielding increasing quantities of data in the psychological sciences, such ML approaches promise high predictive performance. As RF does not inherently 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 s

SUBMITTER: Buczak P 

PROVIDER: S-EPMC11971599 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature