For GBM and randomForests:
I understand, that when I set nfolds to 10, the training frame is divided into 2 sets, first having 90% of rows, and second having 10% of rows randomly for first cross validation model, the model is built on these 90% of rows (first set) and validated against the remaining 10% of rows (second set) to provide measures of accuracy i.e. AUC.
This is how the first cross validation (CV) model is built.
The question is, for the 2nd and subsequent out of 10 CV models, will the h2o randomly choose another 10% of the entire training frame (without replacement - so each row is only used once for validation and 9 times for training; or with replacement, any row can be either part of validation or training dataset for each of CV models) ?
To rephrase the question:
Let's say I have a training frame of 10 rows and do a 5 folds cv:
My first CV fold can use rows 1-8 for training and 9-10 for validation,
Second can use only use for validation any out of rows which were not yet used for validation for any of previously build CV models (here any rows besides 9-10)