Random Forest parameters optimization

[mlauber71]

What you could do is try H2O automl and tell it just to use tree algorithms.

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…

“DRF” that is
https://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/algo-params/include_algos.html

If another tree comes out on top and you strictly want random forest I have a KNIME wrapper with hyper parameter search somewhere.

Build a Random Forest model — h2o.randomForest