Each of the four models involved in this work requires the tuning of a number of parameters to ensure optimal performance. We tune the NN parameters manually, and we use the GridSearch algorithm provided by the Python scikit-learn library (GridSearchCV) to tune the parameters of the SVM and RF models. GridSearchCV calculates the best parameter combination by performing a cross-validated grid-search over a parameter grid. The parameters which we optimize for the SVM are: the C and γ, while those of the RF are: number of estimators and maximum depth.