Model-Agnostic Interpretation via Feature Perturbation Visualization - Marcílio Júnior, Wilson E.; Eler, Danilo Medeiros; Breve, Fabrício (Anais da Conference on Graphics, Patterns and Images (SIBGRAPI); 2023: Anais da XXXVI Conference on Graphics, Patterns and Images, 2023)
As machine learning algorithms increasingly replace traditional approaches, ensuring their reliability becomes crucial in applications where incorrect decisions can lead to serious consequences. This work proposes a novel model-agnostic in-terpretation approach using feature perturbations, along with a validated visualization tool. The approach enables better understanding of model decisions by domain experts, facilitating effective decision-making in real-world applications.