I was researching about the supervised algorithm called Random Forest, that made me begin to study about decision trees, and how to induce them from a set, in order to create several predictors. My question comes at this point when we consider functions such as Information Gain or Gini impurity.
In order to use as splitting functions, both functions are very similar almost for the binary case, the only differences is that the first function has a maximum value of 1 whereas the other one has a maximum of 0.5 for the binary case. I would like to know in which cases it's is better to use one instead the another. I would also really appreciate any intuitive explanation of this kind of conclusion.