Method train

nltk/classify/decisiontree.py:137–186

:param binary: If true, then treat all feature/value pairs as individual binary features, rather than using a single n-way branch for each feature.

(
        labeled_featuresets,
        entropy_cutoff=0.05,
        depth_cutoff=100,
        support_cutoff=10,
        binary=False,
        feature_values=None,
        verbose=False,
    )

    @staticmethod
    def train(
        labeled_featuresets,
        entropy_cutoff=0.05,
        depth_cutoff=100,
        support_cutoff=10,
        binary=False,
        feature_values=None,
        verbose=False,
    ):
        """
        :param binary: If true, then treat all feature/value pairs as
            individual binary features, rather than using a single n-way
            branch for each feature.
        """
        # Collect a list of all feature names.
        feature_names = set()
        for featureset, label in labeled_featuresets:
            for fname in featureset:
                feature_names.add(fname)

        # Collect a list of the values each feature can take.
        if feature_values is None and binary:
            feature_values = defaultdict(set)
            for featureset, label in labeled_featuresets:
                for fname, fval in featureset.items():
                    feature_values[fname].add(fval)

        # Start with a stump.
        if not binary:
            tree = DecisionTreeClassifier.best_stump(
                feature_names, labeled_featuresets, verbose
            )
        else:
            tree = DecisionTreeClassifier.best_binary_stump(
                feature_names, labeled_featuresets, feature_values, verbose
            )

        # Refine the stump.
        tree.refine(
            labeled_featuresets,
            entropy_cutoff,
            depth_cutoff - 1,
            support_cutoff,
            binary,
            feature_values,
            verbose,
        )

        # Return it
        return tree

    @staticmethod
    def leaf(labeled_featuresets):