ABSTRACT
A decision tree uses a divide-and-conquer strategy. It attacks a complex problem by dividing it into simpler problems and recursively applies the same strategy to the sub-problems. Solutions of sub-problems can be combined to yield a solution of the complex problem. The power of this approach comes from the ability to split the instance-space into subspaces and each subspace is fitted with different models. Decision trees are one of the most used algorithms in the data mining community: they are distribution-free, and tree models exhibit a high degree of interpretability. These factors strongly contribute to their increasing popularity in the data mining community.
