class_docstr: Logistic regression classifier.
  
  Note that this implementation of Logistic Regression
  expects binary class labels in {0, 1}.
  
  Parameters
  ------------
  eta : float (default: 0.01)
      Learning rate (between 0.0 and 1.0)
  
  epochs : int (default: 50)
      Passes over the training dataset.
      Prior to each epoch, the dataset is shuffled
      if `minibatches > 1` to prevent cycles in stochastic gradient descent.
  
  l2_lambda : float
      Regularization parameter for L2 regularization.
      No regularization if l2_lambda=0.0.
  
  minibatches : int (default: 1)
      The number of minibatches for gradient-based optimization.
      If 1: Gradient Descent learning
      If len(y): Stochastic Gradient Descent (SGD) online learning
      If 1 < minibatches < len(y): SGD Minibatch learning
  
  random_seed : int (default: None)
      Set random state for shuffling and initializing the weights.
  
  print_progress : int (default: 0)
      Prints progress in fitting to stderr.
      0: No output
      1: Epochs elapsed and cost
      2: 1 plus time elapsed
      3: 2 plus estimated time until completion
  
  Attributes
  -----------
  w_ : 2d-array, shape={n_features, 1}
    Model weights after fitting.
  
  b_ : 1d-array, shape={1,}
    Bias unit after fitting.
  
  cost_ : list
      List of floats with cross_entropy cost (sgd or gd) for every
      epoch.
  
  Examples
  -----------
  For usage examples, please see
  https://rasbt.github.io/mlxtend/user_guide/classifier/LogisticRegression/
docstr: Predict class probabilities of X from the net input.
  
  Parameters
  ----------
  X : {array-like, sparse matrix}, shape = [n_samples, n_features]
      Training vectors, where n_samples is the number of samples and
      n_features is the number of features.
  
  Returns
  ----------
  Class 1 probability : float
docstr_tok: ["Predict","class","probabilities","of","X","from","the","net","input",".","Parameters","--","--","--","--","--","X",":","{","array-like",",","sparse","matrix","}",",","shape","=","[","n_samples",",","n_features","]","Training","vectors",",","where","n_samples","is","the","number","of","samples","and","n_features","is","the","number","of","features",".","Returns","--","--","--","--","--","Class","1","probability",":","float"]
code: def predict_proba(self, X):
      """Predict class probabilities of X from the net input.
  
      Parameters
      ----------
      X : {array-like, sparse matrix}, shape = [n_samples, n_features]
          Training vectors, where n_samples is the number of samples and
          n_features is the number of features.
  
      Returns
      ----------
      Class 1 probability : float
  
      """
      return self._forward(X)
  
file_path: mlxtend/mlxtend/classifier/logistic_regression.py
imports: from time import time
  import numpy
  from .._base import _BaseModel, _Classifier, _IterativeModel
label_desc: Use image node_id 7 for calling the LogisticRegression obj's underlying member method code with example usage: obj.predict_proba(X) and returns: self