In this chapter two programs are presented: fit_func_esvr.py and fit_func_nusvr.py In fact through the argument --svrparams the user passes a series of hyper-parameters to adjust the behavior of the'underlying SVR algorithm and others to configure its learning phase. In addition to the parameters of the underlying regressor the program supports its own arguments to allow the user to pass the training dataset and on which file to save the trained model. The format of the input datasets is in csv format (with header), with $n m$ columns, of which the first $n$ columns contain the values of the $n$ independent variables and the last $m$ containing the values of the dependent variables. In this chapter the program predict_func.py is presented and which purpose is to make predictions on a test dataset applying it to a previously trained e-SVR or nu-SVR model respectively via the program fit_func_esvr.py or fit_func_nusvr.py,

In this chapter the programs fit_func_miso.py and fit_func_mimo.py are presented and they are technically wrappers of the class XGBRFRegressor of the XGBoost library and which purpose is to allow the use of the regression of the underlying regressor to fit functions without having to write code but only acting on the command line. In fact through the argument --xgbparams the user passes a series of hyper-parameters to adjust the behavior of the underlying XGBoost regressor algorithm and others to configure its learning phase. In addition to the parameters of the underlying regressor the two programs support their own arguments to allow the user to pass the training dataset and optionally the validation dataset, on which file to save the trained model, the metrics to calculate during the training, constraints for regularization (e.g. The program fit_func_miso.py, as well as the underlying XGBoost regressor, is of type M.I.S.O., i.e. Multiple Input Single Output: it is designed to fit a function of the form $f \colon \rm I\!R n \to \rm I\!R$ where the number of independent variables is arbitrarily large while the output dependent variable is only one.