Counterfactuals are widely used to explain ML model predictions by providing
alternative scenarios for obtaining the more desired predictions. They can be
generated by a variety of methods that optimize different, sometimes
conflicting, quality measures and produce quite different solutions. However,
choosing the most appropriate explanation method and one of the generated
counterfactuals is not an easy task. Instead of forcing the user to test many
different explanation methods and analysing conflicting solutions, in this
paper, we propose to use a multi-stage ensemble approach that will select
single counterfactual based on the multiple-criteria analysis. It offers a
compromise solution that scores well on several popular quality measures. This
approach exploits the dominance relation and the ideal point decision aid
method, which selects one counterfactual from the Pareto front. The conducted
experiments demonstrated that the proposed approach generates fully actionable
counterfactuals with attractive compromise values of the considered quality
measures.

基于多标准分析的多阶段集成方法能够从 Pareto 前沿中选择一个用于解释机器学习模型预测的可行对策。实验结果表明，该方法能够生成具有吸引力的多种质量度量的可操作对策。