Performance of the supervised learning algorithms in sex estimation of the proximal femur: A comparative study in contemporary Egyptian and Turkish samples

•Overall and sex-specific accuracies of classifiers are comparable at 0.50 threshold.•Conditional class probabilities vary among classifiers due to different assumptions.•Linear discriminant function is a simple and elegant method for binary classification.•NaïveBayes classified most of cases at 0.95 threshold but calibration is required.•Random forest is the best supervised learning method for sex estimation.