  Decision trees algorithms use a gain function to select the best split during
the tree's induction. This function is crucial to obtain trees with high
predictive accuracy. Some gain functions can suffer from a bias when it
compares splits of different arities. Quinlan proposed a gain ratio in C4.5's
information gain function to fix this bias. In this paper, we present an
updated version of the gain ratio that performs better as it tries to fix the
gain ratio's bias for unbalanced trees and some splits with low predictive
interest.
