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Counterfactual explanations (CFEs) guide users on how to adjust inputs to machine learning models to achieve desired outputs. While existing research primarily addresses static scenarios, real-world applications often involve data or model changes, p
Externí odkaz:
http://arxiv.org/abs/2408.04842
Recent advancements in machine learning have accelerated its widespread adoption across various real-world applications. However, in safety-critical domains, the deployment of machine learning models is riddled with challenges due to their complexity
Externí odkaz:
http://arxiv.org/abs/2408.00986
Publikováno v:
International Journal of Applied Mathematics and Computer Science 34 1 (2024) 119-133
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 measu
Externí odkaz:
http://arxiv.org/abs/2403.13940