Random splitting, which assigns the data randomly into training and test sets, can be considered easier for the machine learning methods than scaffold splitting, which splits the data in the training and test set based on different scaffolds. In this study, we use (1 -ROC-AUC) on a validation set of single-task machine learning methods (including random forest and kNN) trained on each test task as the measure for internal task hardness.