EviNAM: Intelligibility and Uncertainty via Evidential Neural Additive Models
This addresses the need for more intelligible and trustworthy predictions in decision-making, though it appears incremental as an extension of existing methods.
The paper tackled the problem of combining interpretability and uncertainty estimation in machine learning by proposing EviNAM, which integrates Neural Additive Models with evidential learning, and demonstrated that it matches state-of-the-art predictive performance in experiments.
Intelligibility and accurate uncertainty estimation are crucial for reliable decision-making. In this paper, we propose EviNAM, an extension of evidential learning that integrates the interpretability of Neural Additive Models (NAMs) with principled uncertainty estimation. Unlike standard Bayesian neural networks and previous evidential methods, EviNAM enables, in a single pass, both the estimation of the aleatoric and epistemic uncertainty as well as explicit feature contributions. Experiments on synthetic and real data demonstrate that EviNAM matches state-of-the-art predictive performance. While we focus on regression, our method extends naturally to classification and generalized additive models, offering a path toward more intelligible and trustworthy predictions.