class_docstr: Base class for supervised methods (supports only binary classification).
  Should not be used as by itself.
  ----------
  norm : 'l1', 'l2', 'max' or None, optional
      Norm used to normalize term vectors. None for no normalization.
  smooth_df : boolean or int, default=True
      Smooth df weights by adding one to document frequencies, as if an
      extra document was seen containing every term in the collection
      exactly once. Prevents zero divisions.
  sublinear_tf : boolean, default=False
      Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf).
  References
  ----------
class_docstr_tok: ["Base","class","for","supervised","methods","(","supports","only","binary","classification",")",".","Should","not","be","used","as","by","itself",".","--","--","--","--","--","norm",":","'l1","'",",","'l2","'",",","'max","'","or","None",",","optional","Norm","used","to","normalize","term","vectors",".","None","for","no","normalization",".","smooth_df",":","boolean","or","int",",","default=True","Smooth","df","weights","by","adding","one","to","document","frequencies",",","as","if","an","extra","document","was","seen","containing","every","term","in","the","collection","exactly","once",".","Prevents","zero","divisions",".","sublinear_tf",":","boolean",",","default=False","Apply","sublinear","tf","scaling",",","i.e",".","replace","tf","with","1","+","log","(","tf",")",".","References","--","--","--","--","--"]
code: def __init__(self, norm="l2", smooth_df=True, sublinear_tf=False):
      self.norm = norm
      self.smooth_df = smooth_df
      self.sublinear_tf = sublinear_tf
  
imports: import collections
  import itertools
  import numpy
  import scipy.sparse
  from scipy.stats import norm
  from sklearn.decomposition import TruncatedSVD
  from sklearn.preprocessing import normalize
  from sklearn.base import TransformerMixin, BaseEstimator
  from gensim.models.keyedvectors import BaseKeyedVectors
label_desc: Use image node_id 1 to create a new BaseBinaryFitter object from inherited base classes: TransformerMixin with example: obj = BaseBinaryFitter(norm, smooth_df, sublinear_tf)