I'm trying to create a k-Nearest-Neighbor based model of 76-dimensional input data $I$ and 44-dimensional output data $O$. Through domain knowledge I know that only certain input dimensions are related to certain output dimensions.
Let the mapping be as follows:
I[0:35] -> O[0:20] I[35:37] -> O[20:23] I[37:76] -> O[23:43]
Is there a possibility to combine three separate k-Nearest-Neighbors models (all with their own hyperparameter settings) to achieve the desired setup?
End result should be fast computation of:
I -> [combined model] -> O
sklearn's MultiOutputRegressor only takes single output models, which is not what I'm looking for.