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I have been experimenting with some of the linear tree formulations within OMLT. My use case is a linear tree model with 9 inputs and 12 outputs using LinearTreeGDPFormulation.
I've noticed that n_outputs is hard coded to 1 in LinearTreeDefinition. For representing multi-output models, would the only current strategy be to train each of the 12 outputs separately and create separate OMLT blocks?
The text was updated successfully, but these errors were encountered:
@zkilwein Thanks for raising this issue. Yes that is currently the only method for supporting these multioutput trees, however I am working on a rewrite of how we handle trees and ensembles in OMLT.
I have been experimenting with some of the linear tree formulations within OMLT. My use case is a linear tree model with 9 inputs and 12 outputs using LinearTreeGDPFormulation.
I've noticed that n_outputs is hard coded to 1 in LinearTreeDefinition. For representing multi-output models, would the only current strategy be to train each of the 12 outputs separately and create separate OMLT blocks?
The text was updated successfully, but these errors were encountered: