Model-Based Counterfactual Explanations Incorporating Feature Space Attributes for Tabular Data

arXiv:2404.13224v1 Announce Type: new
Abstract: Machine-learning models, which are known to accurately predict patterns from large datasets, are crucial in decision making. Consequently, counterfactual explanations-methods explaining predictions by introducing input perturbations-have become prominent. These perturbations often suggest ways to alter the predictions, leading to actionable recommendations. However, the current techniques require resolving the optimization problems for each input change, rendering them computationally expensive. In addition, traditional encoding methods inadequately address the perturbations of categorical variables in tabular data. Thus, this …

arxiv counterfactual cs.lg data feature space tabular tabular data type