Abstract: The application of machine learning technology in the financial field is becoming more and more prevalent,and providing interpretable machine learning methods to users has become an important research topic.In recent years,counterfactual explanation has attracted widespread attention,which improves the interpretability of machine learning models by providing perturbation vectors to change the predicted results obtained by classifiers.However,existing methods face feasibility and operability issues in generating counterfactual instances.This paper proposes a new counterfactual explanation framework that introduces the concept of feature-variable cost weight matrix,considering the ease of changing different feature variables to make the counterfactual results more realistic and feasible.At the same time,by predefining the feature-variable cost weight matrix by experts,a feasible method for calculating the cost weight of feature variables is proposed,allowing users to make personalized adjustments according to actual situations.The defined objective function comprehensively considers three indicators:feature-weighted distance,sparsity,and proximity,ensuring the feasibility,simplicity,and closeness to the original sample set of counterfactual results.Genetic algorithms are used to solve the problem and generate the optimal action plan.Through experiments on real datasets,it is confirmed that our method can generate feasible and actionable counterfactual instances compared to existing counterfactual me-thods.

Key words: Machine learning, Interpretability, Counterfactual explanation, Weight matrix, Genetic algorithm