Summary
In this chapter, you were introduced to the concepts of XAI, causality, and counterfactuals. After getting acquainted with the German Credis Risk dataset, we created a machine learning model that predicts whether an applicant is creditworthy. Next, we applied genetic algorithm-based counterfactual analysis of the dataset to the trained model, explored several “what-if” scenarios, and gained valuable insights.
In the next two chapters, we will shift our focus to accelerating the execution of genetic algorithm-based programs, such as the ones we’ve developed throughout this book, by exploring different strategies for applying concurrency.
