Hi I’m using the H2O GBM Regression nodes and would like to know the ranking of variables in terms of which have the most impact on training the model. I think I’ve read somewhere before that this is possible using the H2O nodes. Can anyone point me in the right direction of how to accomplish this? Kind Regards Brendan

You could just use the learner [image] H2O Gradient Boosting Machine Learner Learns a Gradient Boosting Machine (GBM) classification model using H2O . [image] Or you could use the H2O Automl wrapper and exclude all methods besides GBM :slight_smile: [image] H2O.ai AutoML in KNIME for classification problems Knowledge sharing 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…