Metric Tools for Sensitivity Analysis with Applications to Neural Networks
In this paper, a theoretical framework is proposed to study sensitivities of Machine Learning models using metric techniques. From this metric interpretation, a complete family of new quantitative metrics called α-curves is extracted. These α-curves provide information with greater depth on the importance of the input variables for a machine learning model than existing XAI methods in the literature. We demonstrate the effectiveness of the α-curves using synthetic and real datasets, comparing the results against other XAI methods for variable importance and validating the analysis results with the ground truth or literature information.
Email Address of Submitting Authorjpizarroso@comillas.edu
ORCID of Submitting Author0000-0002-1806-9191
Submitting Author's InstitutionPontifical Comillas University
Submitting Author's Country