HyConEx: Hypernetwork classifier with counterfactual explanations for tabular data

ArXi:2503.12525v2 Announce Type: replace-cross In recent years, there has been a growing interest in explainable AI methods. In addition to making accurate predictions, we also want to understand what the model's decision is based on. One of the fundamental levels of interpretability is to provide counterfactual examples explaining the rationale behind the decision and identifying which features, and to what extent, must be modified to alter the model's outcome. To address these requirements, we