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How to use an output of one model as an input to another one?
I have this dataset where I have 10 attributes and two labels, say, Label X and Y. I built individual models for each labels using linear regression and performance was okay but it could be better....but I noticed that the two labels, X and Y are highly correlated and if I use label X as a feature when modeling for Label Y, the accuracy of the model improves considerably. but I can not use Label X as an input feature in deployment as I don't know the value yet. and trying to Predict Label X using an individual model and using it to predict Label Y in another model would not consider the error propagation and thus, it may result in a model performance that is too optimistic.
so I am trying to find a way to develop a sort of parent model that allows me to use the 10 features I have to first predict Label X and then use that result and again, the 10 features, to predict Label Y. I read that Stacking requires that the base learner and the stacking model learner have similar labels so I cannot use that. Is there any other way to do this?
Thank You for taking the time to help.