I've started reading a machine learning book since a few days ago, and I've learned about how it can be used for classification/regression/etc. However, I am unsure if it will be able to handle the task I want to accomplish.
My goal is to build a machine learning algorithm that predicts the return type of a method based on its name and other information. For example, if someone is writing
in an IDE, and
GetUsers() is not yet defined, I might want to predict that its return type is
List<User> and offer autocompletion based on that. I plan to pre-train my model on lots of code on the web, where it will try to guess the type, and then compare it against the actual type (it would be a supervised training model).
My problem is: I'm not sure how to model this as a classification/regression task. A library may have many types, plus you can nest generic types like so:
Task<List<User>> to build an infinite number of types.
How should I approach this problem?