  This research paper delves into the innovative integration of Shannon entropy
and rough set theory, presenting a novel approach to generalize the evaluation
approach in machine learning. The conventional application of entropy,
primarily focused on information uncertainty, is extended through its
combination with rough set theory to offer a deeper insight into data's
intrinsic structure and the interpretability of machine learning models. We
introduce a comprehensive framework that synergizes the granularity of rough
set theory with the uncertainty quantification of Shannon entropy, applied
across a spectrum of machine learning algorithms. Our methodology is rigorously
tested on various datasets, showcasing its capability to not only assess
predictive performance but also to illuminate the underlying data complexity
and model robustness. The results underscore the utility of this integrated
approach in enhancing the evaluation landscape of machine learning, offering a
multi-faceted perspective that balances accuracy with a profound understanding
of data attributes and model dynamics. This paper contributes a groundbreaking
perspective to machine learning evaluation, proposing a method that
encapsulates a holistic view of model performance, thereby facilitating more
informed decision-making in model selection and application.
