Tree models are ubiquitous in machine learning. They are naturally suited to divide and conquer iterative algorithms. One of the main advantages of decision tree models is that they are naturally easy to visualize and conceptualize. They allow inspection and do not just give an answer. For example, if we have to predict a category, we can also expose the logical steps that give rise to a particular result. Also tree models generally require less data preparation than other models and can handle numerical and categorical data. On the down side, tree models can create overly complex models that do not generalize to new data very well. Another potential problem with tree models is that they can become very sensitive to changes in the input data and, as we will see later, this problem can be mitigated against using them as ensemble learners.
An important difference between decision trees and the hypothesis mapping used in the previous section is that the tree model does not use internal...
