I am aware of how the M5 regression model trees work. I know that they fit linear regression models at every leaf of the regression tree and that every parent in the node is also associated with a regression model. Furthermore, I even understand that in the M5 regression model, it is possible to perform smoothing by considering the models in the nodes above the leaf.
Now, what is the difference between such a regression model such as M5 as described above and the cubist model? Does cubist do anything different from the above explanation?
I have used the methods in R