Fig. 2.

Radar charts plotting sensitivity (TPR) and specificity (TNR) in the validation data set for all ML models developed with the RUS, ROS, and SMOTE resampled training data and after applying cost-sensitive learning to the 3 best-performing ML models (RF, NB, and LMT). Abbreviations: LMT = logistic model tree; ML = machine learning; NB = naïve Bayes; RF = random forest; ROS = random over-sampling; RUS = random under-sampling; SMOTE = synthetic minority oversampling technique; TNR = true negative rate; TPR = true positive rate.