The objective of this script is to perform a k-fold cross validation of an ensemble built from a dataset. The algorithm:
Divides the dataset in k parts.
Holds out the data in one of the parts and builds an ensemble with the rest of data.
Evaluates the ensemble with the hold out data.
The second and third steps are repeated with each of the k parts, so that k evaluations are generated.
Finally, the evaluation metrics are averaged to provide the cross-validation metrics.
The output of the script will be an evaluation ID. This evaluation is a cross-validation, meaning that its metrics are averages of the k evaluations created in the cross-validation process.