Multicollinearity in Decision Tree Can anybody please explain the affect of multicollinearity on Decision Tree algorithms (Classification and regression).
I have done some searching but was not able to find the right answer as some say it affects it and others say it doesn't.
Desicion trees make no assumptions on relationships between features. It just constructs splits on single features that improves classification, based on an impurity measure like Gini or entropy. If features A, B are heavily correlated, no /little information can be gained from splitting on B after having split on A. So it would typically get ignored in favor of C.

Of course a single decision tree is very vulnerable to overfitting, so one must either limit depth, prune heavily or preferly average many using an ensemble. Such problems get worse with many features and possibly also with co-variance but this problem occurs independently from multicolinearity.