In statistical terms, input partitioning is defined as the division of the initial input into two distinct samples: a treatment sample, also referred to as a training partition, and a control sample, also known as a validation partition. Accordingly, the most crucial element to understand in input partitioning is the representativeness of the samples/partitions. The term “representativeness” is defined as the homogeneous composition of characteristics between the training and validation partitions.