Natural Language Processing with Python

Decision Trees

In the next three sections, we’ll take a closer look at three machine learning methods that can be used to automatically build classification models: decision trees, naive Bayes classifiers, and Maximum Entropy classifiers. As we’ve seen, it’s possible to treat these learning methods as black boxes, simply training models and using them for prediction without understanding how they work. But there’s a lot to be learned from taking a closer look at how these learning methods select models based on the data in a training set. An understanding of these methods can help guide our selection of appropriate features, and especially our decisions about how those features should be encoded. And an understanding of the generated models can allow us to extract information about which features are most informative, and how those features relate to one another.

A decision tree is a simple flowchart that selects labels for input values. This flowchart consists of decision nodes, which check feature values, and leaf nodes, which assign labels. To choose the label for an input value, we begin at the flowchart’s initial decision node, known as its root node. This node contains a condition that checks one of the input value’s features, and selects a branch based on that feature’s value. Following the branch that describes our input value, we arrive at a new decision node, with a new condition on the input value’s features. We continue following the branch selected by each node’s condition, ...