On each iteration, we randomly selected a subset of essays such that the number of text segments included in the test set were greater than 10 but less than 15 and then training Rainbow using the remaining text segments.
carmelTc uses the same set of training data, but unlike the other approaches, it uses its training data in two separate parts, namely one to train the Rainbow models it uses to produce the Rainbow prediction that is part of the vector representation it builds for each text and one to train the decision trees.