The first step of creating a model capable to predict PAAD processing results is feature analysis (FA). The main purpose of this step is to identify (and reduce) the number of relevant parameters for trading-off predictive power and size of the input dataset (given the physical effort associated with each experimental campaign to be used for model training). The process of feature analysis is summarized schematically in Figure . In short, FA is to first filter the input and output feature groups for internal correlations.