class_docstr: An abstract class that wraps around the nltk.classify module.
  
  Expects that descendant classes include a class variable ``nltk_class``
  which is the class in the nltk.classify module to be wrapped.
  
  Example: ::
  
      class MyClassifier(NLTKClassifier):
          nltk_class = nltk.classify.svm.SvmClassifier
class_docstr_tok: ["An","abstract","class","that","wraps","around","the","nltk.classify","module",".","Expects","that","descendant","classes","include","a","class","variable","``","nltk_class","``","which","is","the","class","in","the","nltk.classify","module","to","be","wrapped",".","Example",":",":",":","class","MyClassifier","(","NLTKClassifier",")",":","nltk_class","=","nltk.classify.svm.SvmClassifier"]
code: def __init__(
      self,
      train_set,
      feature_extractor=basic_extractor,
      format=None,
      **kwargs,
  ):
      super(NLTKClassifier, self).__init__(
          train_set, feature_extractor, format, **kwargs
      )
      self.train_features = [
          (self.extract_features(d), c) for d, c in self.train_set
      ]
  
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 1 to create a new NLTKClassifier object from inherited base classes: BaseClassifier with example: obj = NLTKClassifier(train_set, feature_extractor, format)