title: Exact definitions of loss functions in sklearn.linear_model.SGDClassifier
body: <p>I know that I may change loss function to one of the following:</p>
  
  <blockquote>
    <p>loss : str, 'hinge' or 'log' or 'modified_huber'
       The loss function to be used. Defaults to 'hinge'. The hinge loss is
       a margin loss used by standard linear SVM models. The 'log' loss is
       the loss of logistic regression models and can be used for
       probability estimation in binary classifiers. 'modified_huber'
       is another smooth loss that brings tolerance to outliers.</p>
  </blockquote>
  
  <p>But what the definitions of this functions?<br>
  I understand that <code>hinge</code> is <code>max(0, 1 - margin)</code>. And what are others too?</p>