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.
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
.