Explainable Ordinal Factorization Model: Deciphering the Effects of Attributes by Piece-wise Linear Approximation
This work addresses the need for better interpretability in ordinal regression models for managerial problems, offering an incremental improvement over existing methods by characterizing attribute contributions at different value scales.
The paper tackles the problem of explaining how attributes affect predictions in ordinal regression, such as credit scoring and clinical diagnosis, by proposing the Explainable Ordinal Factorization Model (XOFM), which uses piece-wise linear functions and an ordinal transformation process to achieve superior explainability and state-of-the-art prediction accuracy.
Ordinal regression predicts the objects' labels that exhibit a natural ordering, which is important to many managerial problems such as credit scoring and clinical diagnosis. In these problems, the ability to explain how the attributes affect the prediction is critical to users. However, most, if not all, existing ordinal regression models simplify such explanation in the form of constant coefficients for the main and interaction effects of individual attributes. Such explanation cannot characterize the contributions of attributes at different value scales. To address this challenge, we propose a new explainable ordinal regression model, namely, the Explainable Ordinal Factorization Model (XOFM). XOFM uses the piece-wise linear functions to approximate the actual contributions of individual attributes and their interactions. Moreover, XOFM introduces a novel ordinal transformation process to assign each object the probabilities of belonging to multiple relevant classes, instead of fixing boundaries to differentiate classes. XOFM is based on the Factorization Machines to handle the potential sparsity problem as a result of discretizing the attribute scales. Comprehensive experiments with benchmark datasets and baseline models demonstrate that the proposed XOFM exhibits superior explainability and leads to state-of-the-art prediction accuracy.