I trained a random forest model (for regression) and saved it via the following nodes:
Random Forest Learner (Regression) → Tree Ensemble Model Extract → Table to PMML Ensemble → PMML Writer
In a new workflow I loaded the model via the PMML Reader Node. Howerever, when I try to make predictions with the PMML Predictor node, I get the following error message:
ERROR PMML Predictor
Execute failed: The multiple model method ‘majorityVote’ is not suitable for regression
I also tried, after loading the model via the PMML Reader Node, to apply the following nodes to get an input model for the Random Forest Predictor Regression Node directly:
PMML Reader → PMML To Cell → Cell To Model
Cell To Model however, gives me the following error
The dialog cannot be opened. No column in spec compatible to ‘PortObjectValue’.
@ElenaSW welcome to the KNIME forum. You might have to use the
Notice that the simple PMML Predictor node cannot deal with ensemble models. Here you need to use the PMML Ensemble Predictor node to implement the majority vote.
The model combination methods listed above are applicable as follows:
(…)
For regression models only average, weightedAverage, median, weightedMedian, sum, weightedSum, modelChain, selectFirst, or selectAll are applicable. The first six methods are applied to the predicted values of all models for which the predicate evaluates to true.
I couldn’t upload the executed workflow with its uncompressed data because it is far beyond the forum allowed limit size. Instead, I compressed it and then the workflow handles itself the decompression.
The essential solution are the nodes highlighted within a yellow rectangle in the workflow snapshot above.
Hope it helps. Otherwise please reach out and we will try to help.
