Various post-hoc interpretability methods exist to evaluate the results of machine learning classification and prediction tasks. To better understand the performance and reliability of such methods, which is particularly necessary in high-risk applications, Turbe et al. have developed a framework for quantitative comparison of post-hoc interpretability approaches in time-series classification.
- Hugues Turbé
- Mina Bjelogrlic
- Gianmarco Mengaldo
