H2O.ai AutoML (wrapped with R) in KNIME for regression problems

H2O.ai AutoML (wrapped with R) in KNIME for regression problems - a powerful auto-machine-learning framework https://hub.knime.com/mlauber71/spaces/Public/latest/automl/ kn_automl_h2o_regression_python https://forum.knime.com/u/mlauber71/summary It features various models like Random Forest or XGBoost along with Deep Learning. It has warppers 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 hyper parameter optimization as well. The best model of each round is stored and some graphics are produced to see the results. Results are interpreted thru various statistics and model characteristics are stored in and Excel und TXT file as well as in PNG graphics you can easily re-use in presentations and to give your winning models a visual inspection. Also, you could use the Metanode “Model Quality Classification - Graphics” to evaluate other binary classification models To run this workflow you have to install Python and H2O.ai as well as R and several packages. Please refer to the green box on the right. The results may be used also on Big Data clusters with the help of H2O.ai Sparkling Water (https://hub.knime.com/mlauber71/spaces/Public/latest/kn_example_h2o_sparkling_water)

This is a companion discussion topic for the original entry at https://kni.me/w/k44cz3KK8HQ-e8vY