I have a ML problem where I want to divide the prediction task into subproblems (where I believe specialized models will do better). All these predictions tasks operate independently and will use the same input data - but will have different estimators/targets.
- single dataset (A)
- shared transformations A -> B
- estimator #1: random forests with target Y1
- estimator #2: GBM classifier with target Y2
- estimator #3: logistic regression with target Y2
- the predictions of each of these models will be output as a tuple (#1, #2, #3)
I'm looking for a simple (or best practice way) to define the above pipeline and train it and be able to use it for prediction. I have looked at sklearn Pipeline but best I can tell you can't use that to have multiple estimators for training/predictions (would love to learn I'm wrong on this).
My fallback option is to build a class that supports
predict_proba but under the hood just calls these models sequentially (training in sequence & generating predictions in sequence before returning the tuple of results).
Is there a better way to go about this problem?