How to combine two different machine learning models, to get the combined result? To further explain my question, I will explain my use-case. Say I have a model which is trained for how good/bad a food is for obesity based on its nutrition facts. And another model for, say hypertension. I wish to combine these models to be able to predict food which are good for a person suffering from both obesity and hypertension.
I do not wish to retrain a new model for both cases as eventually I will add more diseases and do not wish to train for each combination of diseases.
Ideally I would like to be able to combine different models (eg Logistic Regression, SVM) but as a stepping stone, I was looking to combine two models of the same type.
I am not sure if Stacking would be appropriate in this case.
Train another XGB model on second layer. Your first layer contains all the individual model and in the second layer you will train another model on top of all of them. XGB is good for this task.