P$^2$CE: Model-Agnostic Plausible Pareto-Optimal Counterfactual Explanations

In three linesP²CE generates plausible Pareto-optimal counterfactual explanations for ML models. The algorithm uses isolation forests and SHAP values to balance feasibility, plausibility, and computational efficiency. Evaluated on 3 datasets, it outperforms existing methods in solution quality and speed.

Summary generated by Claude — human-verified