# Relation between decision tree Depth and number of Attributes

In Machine Learning libraries such as weka, we can set a tree to be of infinite Depth with maxDepth = -1. I am curious to know what would happen if trees were set to a depth far higher than the number of attributes/features available. In other words, what if we went on reverse-pruning spree? Would it lead to overfitting the data or would it cause the tree to perform much better?

You cannot actually set the depth of the tree, only the maximal possible depth. In this case, the tree stops growing (or, to be more precise, the node stops expanding) as soon as splitting criterion (typically Gini impurity or information gain) is below some threshold (typically 0). Increasing the minimum number of instances per leaf (minNumObj in Weka) is another reason for tree to stop growing.