Universal Predictor

There are forecasting models, generated by different learner nodes inside other workflow. To interpret each model dedicated Predictor node is needed (e.g. Tree Ensemble Predictor/Weka Predictor).

  1. Is there any components (like “Universal Predictor”) or other solutions to interpret wide range of models (PMML Predictor don’t cover all possible variants)?
  2. When reading the model, how can I know it type to suggest dedicated Predictor node?

Hi @Alex_Vilnius,
I think there is a good reason why there isn’t such a universal predictor. There are different types of predictions to make: e.g. numerical values, categorical values, probabilities or forecasts.
Why should this all be squeezed into one super-complex component, with all sorts of different output-types?

In the written model (a zipfile) is a file called object.file which is a zipfile as well. In there is the model.pmml. PMML is a version of XML (https://en.wikipedia.org/wiki/Predictive_Model_Markup_Language), so by scanning on specific tags one could determine the type of model contained in it.
An example of such a tag is:
<GeneralRegressionModel modelType="multinomialLogistic" functionName=... algorithmName="LogisticRegression" modelName="KNIME Logistic Regression" targetReferenceCategory=...>

But personally I would never go down that road. I would try to give those written models distinctive names which somehow show the type of model which is included. A model writer node is linked to a specific learner node, so it should be possible to create useful names.


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