class_docstr: A classifier based on the decision tree algorithm, as implemented in
  NLTK.
  
  :param train_set: The training set, either a list of tuples of the form
      ``(text, classification)`` or a filename. ``text`` may be either
      a string or an iterable.
  :param feature_extractor: A feature extractor function that takes one or
      two arguments: ``document`` and ``train_set``.
  :param format: If ``train_set`` is a filename, the file format, e.g.
      ``"csv"`` or ``"json"``. If ``None``, will attempt to detect the
      file format.
  
  .. versionadded:: 0.6.2
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docstr: Return a string representation of this decision tree that expresses
  the decisions it makes as a nested set of pseudocode if statements.
  
  :rtype: str
docstr_tok: ["Return","a","string","representation","of","this","decision","tree","that","expresses","the","decisions","it","makes","as","a","nested","set","of","pseudocode","if","statements",".",":","rtype",":","str"]
code: def pseudocode(self, *args, **kwargs):
      """Return a string representation of this decision tree that expresses
      the decisions it makes as a nested set of pseudocode if statements.
  
      :rtype: str
      """
      return self.classifier.pseudocode(*args, **kwargs)
  
imports: from __future__ import absolute_import
  from itertools import chain
  import nltk
  from textblob.compat import basestring
  from textblob.decorators import cached_property
  from textblob.exceptions import FormatError
  from textblob.tokenizers import word_tokenize
  from textblob.utils import strip_punc, is_filelike
  import textblob.formats
label_desc: Use image node_id 2 for calling the DecisionTreeClassifier obj's underlying member method code with example usage: obj.pseudocode() and returns: self