H2o - cross validation with hyperparameters tuning

Hi, I am new to KNIME and struggling a bit.

I want to run a h2o randomforest algorithm with parameter maxDepth ranging from 1 to 7. However, I do not want to use the whole data instead divide it into 5 fold (cross-validation) and then identify the best result.

Through various tutorials and forum discussion, I was able to achieve the following workflow but get an error when ending the optimization loop.

Kindly assist.
Thank you.

Hi @vasim07,

the very beginning of your workflow looks good already, but the ends of your CV and hyperparameter optimization need to be restructured. One of our blogposts describes in detail how to do hyperparameter optimization and CV with H2O nodes and it comes with a supplementary workflow. Feel free to use it as a template and adjust it to your needs.

I hope this helps.



Thank you Marten,

This is exactly what I was looking at. thanks.


1 Like

This topic was automatically closed 7 days after the last reply. New replies are no longer allowed.