H2O.ai AutoML in KNIME for classification problems - a powerful auto-machine-learning framework wrapped with KNIME

H2O.ai AutoML in KNIME for classification problems a powerful auto-machine-learning framework https://hub.knime.com/mlauber71/spaces/Public/latest/automl/ kn_automl_h2o_classification_python https://forum.knime.com/u/mlauber71/summary 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 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


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