Ivan Palomares Carrascosa continues a series on decision trees:
But what are the underlying mechanisms that make decision trees so well-suited for various predictive tasks? And what criteria are internally used to construct them? Specifically, how are nodes recursively split as the tree-shaped structure is formed? This article takes a closer look at the inner workings of decision trees, focusing on how branches are created through deliberate, data-driven splitting (spoiler: it certainly doesn’t happen at random).
One of the main principles of CART is around finding efficient splits for trees, and this digs into some of those details.