I understand that decision trees try to put classifiers with high entropy high on the decision tree. However, how does information gain play into this?
Information gain is defined as:
InformationGain = EntropyBefore - EntropyAfter
Does a decision tree try to place classifiers with low information gain at the top of the tree? So entropy is always maximized and information gain is always minimized?
Sorry, I am just a bit confused. Thank you!