Hi, I'm interested about how DR chooses Hyperparameters for each model. Can be that DR chooses them by a Nested Cross Validation technique?
Thanks for your answer!
So If I train models with 5-Fold Cross Validation, the procedure of DataRobot to search hyperparameters would be doing inner 5-Fold CV in each fold of the initial 5 partitions and then choosing the best set hyperparameters along all folds?
Ok, thanks! now is clearer. And if I use a train - validation procedure with DataRobot, the hyperparameters are chosen by the performance in validation set after training in train set? I mean that there is not a cross validation procedure when selecting hyperparameters with this approach, I'm right?