Hey @mlauber71,
I played a little with your workflow. Works pretty well, you provided a nice documentation 
I’ve let it run about 30mins with the default data you have set and get an AUC of 0.9283 (best model is a GBM). If I train an H2O GBM myself within KNIME using 250 trees and a depth of 6 (training time just a few seconds), I get 0.9292, so I am wondering why the autoML result is worse. I can see that the best model you have trained achieves a better score, 0.9293! Did you use a different configuration for H2O AutoML or simply let it run longer? I can see that you changed the early stopping parameters, those may be interesting to tune. Maybe it’s also just the seed 
Another point is: you are using the same data for test set as for validation set. For the used dataset, this probably does not have a bad impact but might make sense to have separate ones for them in general.
Btw, I like the broad usage of KNIME views. Have you seen the newly released Binary Classification Inspector ? Might be a good addition.
Thanks again for sharing this and H2O.ai AutoML in KNIME for regression problems with the community!
Simon