class_docstr: A classifier based on the Naive Bayes 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:: 0.6.0
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docstr: Displays a listing of the most informative features for this
  classifier.
  
  :rtype: None
docstr_tok: ["Displays","a","listing","of","the","most","informative","features","for","this","classifier",".",":","rtype",":","None"]
code: def show_informative_features(self, *args, **kwargs):
      """Displays a listing of the most informative features for this
      classifier.
  
      :rtype: None
      """
      return self.classifier.show_most_informative_features(
          *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 3 for calling the NaiveBayesClassifier obj's underlying member method code with example usage: obj.show_informative_features() and returns: self