Description: Feature Importance Score is a numerical value that indicates the relevance of each feature or variable in a machine learning model. This concept is fundamental in explainable artificial intelligence, as it allows researchers and developers to understand how and why a model makes specific decisions. The score is calculated using various methods, such as the decrease in model performance when a feature is removed or the contribution of each variable to the model’s error. Higher scores indicate that a feature has a significant impact on model performance, while lower scores suggest minimal influence. This approach not only helps improve model interpretability but also facilitates the identification of redundant or irrelevant features, thus optimizing the modeling process. In a context where transparency and trust in AI systems are increasingly important, the Feature Importance Score becomes an essential tool to ensure that models are understandable and justified, allowing end-users and stakeholders to assess the validity of automated decisions.
