title: sklearn LogisticRegression - plot displays too small coefficient
body: <p>I am attempting to fit a logistic regression model to sklearn's iris dataset. I get a probability curve that looks like it is too flat, aka the coefficient is too small.  I would expect a probability over ninety percent by sepal length > 7 :</p>
  
  <p><a href="https://i.stack.imgur.com/IHTL1.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/IHTL1.png" alt="enter image description here"></a></p>
  
  <p>Is this probability curve indeed wrong?  If so, what might cause that in my code?</p>
  
  <pre><code>from sklearn import datasets
  import matplotlib.pyplot as plt
  import numpy as np
  import math
  
  from sklearn.linear_model import LogisticRegression
  
  data = datasets.load_iris()
  
  #get relevent data
  lengths = data.data[:100, :1]
  is_setosa = data.target[:100]         
  
  #fit model
  lgs = LogisticRegression()
  lgs.fit(lengths, is_setosa)
  m = lgs.coef_[0,0]
  b = lgs.intercept_[0]
  
  #generate values for curve overlay
  lgs_curve = lambda x: 1/(1 + math.e**(-(m*x+b)))         
  x_values = np.linspace(2, 10, 100)
  y_values = lgs_curve(x_values)
  
  #plot it
  plt.plot(x_values, y_values)
  plt.scatter(lengths, is_setosa, c='r', s=2)
  plt.xlabel("Sepal Length")
  plt.ylabel("Probability is Setosa")
  </code></pre>