Feature selection. Because the training set was made of 13638 examples and the generated features were 37, a features selection step was not mandatory. However, we decided to filter the features to obtain a more parsimonious model. We resorted to a filtering strategy because the use of wrapper methods would have made the cross-validation approaches (and in particular the leaveone-out strategy) computationally very demanding. We have applied three different procedures for feature selection, thus obtaining three different subsets of features. N Subset C. Feature selection with the LASSO method (least absolute shrinkage and selection operator) [45] .