Decision Tree, Entropy, Information Gain, Gini Index.
tree. If α=0, then we are minimizing the training loss. The tree will be the same as the original tree. However, with α>0, we add a penalty term which increases with the number of terminal nodes |T|. This means the overall cost gets minimized for a smaller subtree. Minimal cost complexity pruning