Using SHAP Values to Validate Model’s Uncertain Decision for ML-based Lightpath Quality-of-Transmission Estimation

We apply Quantile Regression (QR) for lightpath quality-of-transmission (QoT) estimation with the aim of identifying uncertain decisions and then exploit Shapley Additive Explanations (SHAP) to quantify lightpath features’ importance by means of SHAP values and validate the model’s decisions in a post-processing phase. Numerical results show that our approach can eliminate more than 98% of false predictions and that SHAP values can validate up to 90% of the model's uncertain decisions.