Random Forest parameters optimization

[P_G]

Hi there,

I try to do some parameters optimization on a model (Random forest, for example).
I know that I can do that with a loop.
But I think that I’ve seen in a workflow a more practical node to do that and I can’t find it anymore …
I would have say that its name was Auto Random Forest, but it’s not.
Can you give me a clue on this magical node ? But it was maybe in my dreams …

Thank you for your help

P

[HansS]

Hi @P_G and welcome to the KNIME forum

Do you mean this Component from the Hub? Inside the component (open= cntrl click click) you see the individual nodes. You can eventually adjust and add parameters in the Paramater Optimization Loop Start. More examplas can be found on the KNIME Hub.

gr. Hans

[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

[P_G]

Hello HansS,

Thank you for your reply.
Yes, I think that it is the component I was looking for.
Thank you very much.

P

[P_G]

Hello mlauber71,

Thank you for your answer.
I allready know H2O library but thank you for the time you gave me ;).

P