Training a decision tree classifier

The DecisionTreeClassifier class works by creating a tree structure, where each node corresponds to a feature name and the branches correspond to the feature values. Tracing down the branches, you get to the leaves of the tree, which are the classification labels.

How to do it...

Using the same train_feats and test_feats variables we created from the movie_reviews corpus in the previous recipe, we can call the DecisionTreeClassifier.train() class method to get a trained classifier. We pass binary=True because all of our features are binary: either the word is present or it's not. For other classification use cases where you have multivalued features, you will want to stick to the default binary=False.

Note

In this context, binary refers to feature values, and is not to be confused with a binary classifier. Our word features are binary because the value is either True or the word is not present. If our features could take more than two values, we would have...