I am building a Random Forest model using a grid search with the H2O Python API. I split the data in train and validation and use k-fold cross validation to select the best model in the grid search.
I am able to retrieve the model with the best
g is your grid object, then:
will give you the models ordered by AUC. The 2nd parameter of False means highest AUC will be first. It returns a
H2OTwoDimTable object, so you can select the first model (the best model, by AUC) that way.
I believe it should be sorting based on scores on the validation set, not training set. However you can specify it explicitly with: