Decision tree learner seems to be overfitting

Hi @mlauber71 do you have (by any chance) also a detailed workflow using PMML Nodes (model appender, ensemble,…) I have not found an example on the hub and was curious about that.
thanks and best

@Daniel_Weikert well in fact I had a collection that would try and pack some transformations into a PMML ‘collection’ then the model and then do just one compiled transformation and one for the model. But the whole concept seems to be buggy and would crash all the time. Maybe I put that on the hub, just to play around with.

I think I once collected some Spark transformations into a PMML, compiled that and executed it successfully on a Big Data cluster.

But it seems these PMML things are not really well maintained …

OK and since we are hijacking this thread. An additional information for @akhtarameen. There in fact is a node that would convert Gradient Boosted trees to PMML. So there might be another chance to get a more advanced model into PMML-production (not sure how stable that would be …).

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Thanks for sharing your experience. Seems I need to think twice before I decide to dive deeper this rabbit hole with bugs.

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@Daniel_Weikert with PMML it is complicated. There are a lot of nodes that would support PMML in principal but not all would warn you if there is incompatibility and sometimes the problem would only occur after you saved the transformation/model and applied it to a new dataset - which is what you might have to do if you want to put it into production.

This is why I sometimes try to store data preparation steps in SQL code that has been written by KNIME. Maybe not the most elegant way but quite stable. And if you use PMML I would try only to use it step by step and store the individual transformations in a step.

On very large data tables on a big data system and if you have extensive operations like replacing strings with numbers (globally), then you might benefit from compiling transformations for Spark.

OK since we are still hijacking @akhtarameen thread :slight_smile: - another model ensemble which might be converted to PMML:

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