Model-Agnostic Interpretation via Feature Perturbation Visualization
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Abstract
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.
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Brazilian Symposium of Computer Graphic and Image Processing, p. 19-24.
