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

29 May 2020Kevin FauvelVéronique MassonÉlisa Fromont

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 operationalize the performance-explainability assessment of existing machine learning methods... (read more)

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