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

Understand

Our research aims to propose a new performance-explainability analytical framework to assess and benchmark machine learning methods.

• The framework details a set of characteristics that systematize the performance-explainability assessment of existing machine learning methods.
• In order to illustrate the use of the framework, we apply it to benchmark the current state-of-the-art multivariate time series classifiers.