class_docstr: Abstract classifier class from which all classifers inherit. At a
  minimum, descendant classes must implement a ``classify`` method and have
  a ``classifier`` property.
  
  :param train_set: The training set, either a list of tuples of the form
      ``(text, classification)`` or a file-like object. ``text`` may be either
      a string or an iterable.
  :param callable feature_extractor: A feature extractor function that takes one or
      two arguments: ``document`` and ``train_set``.
  :param str format: If ``train_set`` is a filename, the file format, e.g.
      ``"csv"`` or ``"json"``. If ``None``, will attempt to detect the
      file format.
  :param kwargs: Additional keyword arguments are passed to the constructor
      of the :class:`Format <textblob.formats.BaseFormat>` class used to
      read the data. Only applies when a file-like object is passed as
      ``train_set``.
  
  .. versionadded:: 0.6.0
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docstr: Trains the classifier.
docstr_tok: ["Trains","the","classifier","."]
code: def train(self, labeled_featureset):
      """Trains the classifier."""
      raise NotImplementedError('Must implement a "train" method.')
  
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 5 for calling the BaseClassifier obj's underlying member method code with example usage: obj.train(labeled_featureset) without return types