old_contents: import numpy as np
  from . import _liblinear
  from .svm import BaseLibLinear
  
  class LogisticRegression(BaseLibLinear):
      """
      Logistic Regression.
  
      Implements L1 and L2 regularized logistic regression.
  
      Parameters
      ----------
      X : array-like, shape = [n_samples, n_features]
          Training vector, where n_samples in the number of samples and
          n_features is the number of features.
      Y : array, shape = [n_samples]
          Target vector relative to X
  
      penalty : string, 'l1' or 'l2'
          Used to specify the norm used in the penalization
  
      C : float
          Specifies the strength of the regularization. The smaller it is
          the bigger in the regularization.
  
      intercept : bool, default: True
          Specifies if a constant (a.k.a. bias or intercept) should be
          added the decision function
  
      Attributes
      ----------
  
      `coef_` : array, shape = [n_classes-1, n_features]
          Coefficient of the features in the decision function.
  
      `intercept_` : array, shape = [n_classes-1]
          intercept (a.k.a. bias) added to the decision function.
          It is available only when parameter intercept is set to True
  
      Methods
      -------
      fit(X, Y) : self
          Fit the model
  
      predict(X) : array
          Predict using the model.
  
      See also
      --------
      LinearSVC
  
      References
      ----------
      LIBLINEAR -- A Library for Large Linear Classification
      http://www.csie.ntu.edu.tw/~cjlin/liblinear/
      """
  
      def __init__(self, penalty='l2', eps=1e-4, C=1.0, has_intercept=True):
          super(LogisticRegression, self).__init__ (penalty=penalty, loss='lr',
              dual=False, eps=eps, C=C, has_intercept=has_intercept)
  
      def predict_proba(self, T):
          T = np.asanyarray(T, dtype=np.float64, order='C')
          return _liblinear.predict_prob_wrap(T, self.raw_coef_, self._get_solver_type(),
                                        self.eps, self.C,
                                        self._weight_label,
                                        self._weight, self.label_,
                                        self._get_bias())
new_contents: import numpy as np
  from . import _liblinear
  
  from .base import ClassifierMixin
  from .svm import BaseLibLinear
  
  class LogisticRegression(BaseLibLinear, ClassifierMixin):
      """
      Logistic Regression.
  
      Implements L1 and L2 regularized logistic regression.
  
      Parameters
      ----------
      X : array-like, shape = [n_samples, n_features]
          Training vector, where n_samples in the number of samples and
          n_features is the number of features.
      Y : array, shape = [n_samples]
          Target vector relative to X
  
      penalty : string, 'l1' or 'l2'
          Used to specify the norm used in the penalization
  
      C : float
          Specifies the strength of the regularization. The smaller it is
          the bigger in the regularization.
  
      intercept : bool, default: True
          Specifies if a constant (a.k.a. bias or intercept) should be
          added the decision function
  
      Attributes
      ----------
  
      `coef_` : array, shape = [n_classes-1, n_features]
          Coefficient of the features in the decision function.
  
      `intercept_` : array, shape = [n_classes-1]
          intercept (a.k.a. bias) added to the decision function.
          It is available only when parameter intercept is set to True
  
      Methods
      -------
      fit(X, Y) : self
          Fit the model
  
      predict(X) : array
          Predict using the model.
  
      See also
      --------
      LinearSVC
  
      References
      ----------
      LIBLINEAR -- A Library for Large Linear Classification
      http://www.csie.ntu.edu.tw/~cjlin/liblinear/
      """
  
      def __init__(self, penalty='l2', eps=1e-4, C=1.0, has_intercept=True):
          super(LogisticRegression, self).__init__ (penalty=penalty, loss='lr',
              dual=False, eps=eps, C=C, has_intercept=has_intercept)
  
      def predict_proba(self, T):
          T = np.asanyarray(T, dtype=np.float64, order='C')
          return _liblinear.predict_prob_wrap(T, self.raw_coef_, self._get_solver_type(),
                                        self.eps, self.C,
                                        self._weight_label,
                                        self._weight, self.label_,
                                        self._get_bias())
  
subject: Make sure that the logistic regression does inherit from ClassifierMixin.