class_docstr: Two class LogisticRegression with Prejudice Remover
  
  Parameters
  ----------
  C : float
      regularization parameter
  eta : float
      penalty parameter
  fit_intercept : bool
      use a constant term
  penalty : str
      fixed to 'l2'
class_docstr_tok: ["Two","class","LogisticRegression","with","Prejudice","Remover","Parameters","--","--","--","--","--","C",":","float","regularization","parameter","eta",":","float","penalty","parameter","fit_intercept",":","bool","use","a","constant","term","penalty",":","str","fixed","to","'l2","'"]
code: def __init__(self, C=1.0, eta=1.0, fit_intercept=True, penalty="l2"):
      super(LRwPRType4, self).__init__(
          C=C, eta=eta, fit_intercept=fit_intercept, penalty=penalty
      )
  
      self.coef_ = None
      self.mx_ = None
      self.n_s_ = 0
      self.n_sfv_ = 0
      self.minor_type = 4
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import unicode_literals
  import logging
  import numpy
  from scipy.optimize import fmin_cg
  from sklearn.linear_model import LogisticRegression
  from sklearn.base import BaseEstimator, ClassifierMixin
label_desc: Use image node_id 1 to create a new LRwPRType4 object from inherited base classes: LRwPRObjetiveType4Mixin, LRwPRFittingType1Mixin, LRwPRPredictProbaType2Mixin with example: obj = LRwPRType4(C, eta, fit_intercept, penalty)