I need some help understanding the concept of random forests. As I understand, when I make a decision tree, I carefully select each node so as to maximize the information gain and minimize the entropy, i.e each node should result in a higher information gain than its parent node.
If that is true, then the decision tree is already the best possible learner. Why do I need to combine it with other trees that may not be as good and then take a vote?
If I created the tree to maximize the information gain, then this is already the 'best' model.
I would understand the need for a random forest if I created 10 decision trees by randomly selecting the nodes to split on.