Difference Between KNIME Native ML Nodes and H2O ML Nodes

Hello everyone,

I have a question about the difference between KNIME native ML nodes and H2O ML nodes.

It is often said that native ML nodes are more row-based, while H2O ML nodes use a columnar processing model.

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Do H2O ML nodes always process data in a columnar format by default, without any additional configuration?

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If so, does this generally result in better performance for large datasets compared to native ML nodes?

If anyone has tested this or has insights into the internal processing model or performance characteristics, I would appreciate it if you could share your experience.

Thank you.