new_contents: #One of the simplest options to get a feeling for the "influence" of a given parameter in a linear classification model (logistic being one of those), is to consider the magnitude of its coefficient times the standard deviation of the corresponding parameter in the data.
  
  #Consider this example:
  
  import numpy as np    
  from sklearn.linear_model import LogisticRegression
  
  x1 = np.random.randn(100)
  x2 = 4*np.random.randn(100)
  x3 = 0.5*np.random.randn(100)
  y = (3 + x1 + x2 + x3 + 0.2*np.random.randn()) > 0
  X = np.column_stack([x1, x2, x3])
  
  m = LogisticRegression()
  m.fit(X, y)
  
  # The estimated coefficients will all be around 1:
  print(m.coef_)
  
  # Those values, however, will show that the second parameter
  # is more influential
  print(np.std(X, 0)*m.coef_)
  
  #An alternative way to get a similar result is to examine the coefficients of the model fit on standardized parameters:
  
  m.fit(X / np.std(X, 0), y)
  print(m.coef_)
  
  #Note that this is the most basic approach and a number of other techniques for finding feature importance or parameter influence exist (using p-values, bootstrap scores, various "discriminative indices", etc).
  
  # ref. https://stackoverflow.com/questions/34052115/how-to-find-the-importance-of-the-features-for-a-logistic-regression-model
  
  
subject: Create LogisticRegression_example.py