A Performance-Explainability Framework to Benchmark Machine Learning Methods: Application to Multivariate Time Series Classifiers

In order to match these requirements and conduct experiments to validate the usefulness of the explanations Our research aims to propose a new performanceexplainability by the end-users, there is a need to have a comprehensive analytical framework to assess and assessment of the explainability of the existing methods.