You’ve now seen a few different automatic classifiers, and this chapter will expand on them by introducing a very useful method called decision tree learning. Unlike most other classifiers, the models produced by decision trees are easy to interpret—the list of numbers in a Bayesian classifier will tell you how important each word is, but you really have to do the calculation to know what the outcome will be. A neural network is even more difficult to interpret, since the weight of the connection between two neurons has very little meaning on its own. You can understand the reasoning process of a decision tree just by looking at it, and you can even convert it to a simple series of if-then statements.