title: SKLearn how to get decision probabilities for LinearSVC classifier
body: <p>I am using scikit-learn's linearSVC classifier for text mining. I have the y value as a label 0/1 and the X value as the TfidfVectorizer of the text document. </p>
  
  <p>I use a pipeline like below</p>
  
  <pre><code> pipeline = Pipeline([
      ('count_vectorizer',   TfidfVectorizer(ngram_range=(1, 2))),
      ('classifier',         LinearSVC())
    ])
  </code></pre>
  
  <p>For a prediction, I would like to get the confidence score or probability of a data point being classified as
  <strong>1 in the range (0,1)</strong></p>
  
  <p>I currently use the decision function feature</p>
  
  <pre><code>pipeline.decision_function(test_X)
  </code></pre>
  
  <p>However it returns positive and negative values that seem to indicate confidence. I am not too sure about what they mean either.</p>
  
  <p>However, is there a way to get the values in range 0-1?</p>
  
  <p>For example here is the output of the decision function for some of the data points</p>
  
  <pre><code>    -0.40671879072078421, 
      -0.40671879072078421, 
      -0.64549376401063352, 
      -0.40610652684648957, 
      -0.40610652684648957, 
      -0.64549376401063352, 
      -0.64549376401063352, 
      -0.5468745098794594, 
      -0.33976011539714374, 
      0.36781572474117097, 
      -0.094943829974515004, 
      0.37728641897721765, 
      0.2856211778200019, 
      0.11775493140003235, 
      0.19387473663623439, 
      -0.062620918785563556, 
      -0.17080866610522819, 
      0.61791016307670399, 
      0.33631340372946961, 
      0.87081276844501176, 
      1.026991628346146, 
      0.092097790098391641, 
      -0.3266704728249083, 
      0.050368652422013376, 
      -0.046834129250376291, 
  </code></pre>