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Decision trees algorithms use a gain function to select the best split duringthe tree's induction. This function is crucial to obtain trees with highpredictive accuracy. Some gain functions can suffer from a bias when itcompares splits of different arities. Quinlan proposed a gain ratio in C4.5'sinformation gain function to fix this bias. In this paper, we present anupdated version of the gain ratio that performs better as it tries to fix thegain ratio's bias for unbalanced trees and some splits with low predictiveinterest.

Information gain ratio correction: Improving prediction with more balanced decision tree splits