Decision tree learner seems to be overfitting

[mlauber71]

@akhtarameen

The important thing for me was to convert the trained model into PMML in order to test my model in python. That is why I was limited to using certain learner nodes in KNIME.

@akhtarameen you can import H2O.ai MOJO models into KNIME, R, Python and Spark (and back). That is why I like to use this format - for example I can develop a model on an R machine and then deploy it on a big data cluster.

This collection actually contains Jupiter notebook that basically does the same thing. So you might want to try that. There you might also see how to use relative paths in a notebook.

mlauber71/Public – script

H2O.ai AutoML in KNIME for classification problems

H2O.ai Automl - a powerful auto-machine-learning framework wrapped with KNIME It features various models like Random Forest or XGBoost along with Deep Learning. It has wrappers for R and Python but also could be used from KNIME. The results will be written to a folder and the models will be stored in MOJO format to be used in KNIME (as well as on a Big Data cluster via Sparkling Water). One major parameter to set is the running time the model has to test various models and do some hyperparam…

@akhtarameen

I did run the AutoML node as well but got some different results.

This might be because of different splits in test and training, data preparation with vtreat and longer runtime with model building.

H2O.ai AutoML (wrapped with R community nodes) in KNIME for classification...