A paper titled “Data-driven estimation of a driving safety tolerance zone using imbalanced machine learning” authored by Thodoris Garefalakis, Christos Katrakazas and George Yannis, has been published in Sensors. This paper proposes a framework to identify the level of risky driving behavior as well as the duration of the time spent in each risk level by private car drivers. The results showed that Random Forests and Multilayer Perceptron (MLP) outperformed the rest of the classifiers with 84% and 82% overall accuracy, respectively, and that the maximum speed of the vehicle during a 30-second interval, is the most crucial predictor for identifying the driving time at each safety level.