What's the best method / algorithm for comparing tree changes using machine learning?

I have a problem that I would like to solve using machine learning. I would like to use some sort of classification to see if just an added change in the tree's data structure is "good" or "bad". Let's say I have this tree:

        (A) 
        / \
       /   \
     (B)   (C)

      

        
        
        
      

    

And I apply a change to it (a "good" change, so the algorithm should associate that change with a "good" change). The updated tree will look like this:

       (A)
       / \
      /   \
    (D)   (C)
    /
   /
 (B)

      

        
        
        
      

    

Added a node (D) above another node (B), which will be classified as a "good" change. So when I have a student with the correct data, the algorithm needs to know that if I add a node of type D above a node of type B, it is a "good" change.

I would like to work with XML files that store a tree structure, a simple classifier like naive fills won't work because it won't be able to recognize if a node has been added above another, it just might know that a node has been added.

I don’t know how to use the algorithm / method, and I don’t know how to pass the data to the learner, because the context is important in this scenario.

I'm new to machine learning, so sorry if this is a stupid question.

thank