title: How do I apply scikit-learn's LogisticRegression for some decimal data?
body: <p>I've the training data set like this:</p>
  
  <pre><code>0.00479616 |  0.0119904 |  0.00483092 |  0.0120773 | 1
  0.51213136 |  0.0113404 |  0.02383092 |  -0.012073 | 0
  0.10479096 |  -0.011704 |  -0.0453692 |  0.0350773 | 0
  </code></pre>
  
  <p>The first 4 columns is features of one sample and the last column is its output.</p>
  
  <p>I use scikit this way :</p>
  
  <pre><code>  data = np.array(data)
    lr = linear_model.LogisticRegression(C=10)
  
    X = data[:,:-1]
    Y = data[:,-1]
    lr.fit(X, Y)
  
    print lr
    # The output is always 1 or 0, not a probability number.
    print lr.predict(data[0][:-1])
  </code></pre>
  
  <p>I thought Logistic Regression always should gives a probability number between 0 and 1.</p>