At run time, we frequently split the data into training, validation, and test data. When comparing various hyper-parameters or algorithms, data splitting should be the same throughout all parallel executed evaluations since we want to evaluate the differences between the parameters of interest, not the different data splits. It is worth noting that the data in certain circumstances has time dependencies. In that instance, a time-dependent splitting of the data should be used rather than a random splitting.