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?
Pretty much, yes.
On the inner CV folds that is.
The outer CV isn't used for tuning, only for model selection. And then we have the holdout of course.
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?