Predict by Regions, Explain by Examples: Instance-Level Explanations for Tabular Prediction

Tabular prediction models are widely used in real-world decision-making, making instance-level explanations increasingly important alongside predictive performance. Existing explanation methods largely focus on feature attribution, while example-based explanations often retrieve nearby training instances without ensuring that those examples reflect the structure actually used for prediction. We propose TabERA (Tabular Explainable Region-based Architecture), a tabular prediction framework that grounds prediction and instance-level evidence in the same learned predictive regions. TabERA assigns each input to a region represented by a learned centroid and constructs its prediction from a shared regional component together with an instance-specific correction, yielding an explicit decomposition into a regional baseline and an individual adjustment. The same region is then used to retrieve observed training instances as evidence, while the retrieved cases remain outside the predictive computation. We evaluate TabERA on 21 OpenML classification datasets against 13 representative tabular prediction models. TabERA achieves competitive predictive performance while learning regions with clear label-relevant structure. Across datasets, the regional component accounts for a substantial portion of the predictive decision, and the learned regions generally contain sufficient observed instances to support region-grounded retrieval. These results show that instance-level examples can be tied directly to the model's predictive structure without making retrieved cases part of the prediction itself, providing a practical bridge between tabular prediction and example-based explanation.