title: Learning a binary classifier which outputs probability
body: <p>When, in general, the objective is to build a binary classifier which outputs the probability that an instance is positive, which machine learning would be the most appropriate and in which situation? </p>
  
  <p>In particular, it seems that Support Vector Machines with Platt's Scaling could be a good candidate, but I read around the web that someone uses Kernel Logistic Regression or Gaussian Processes for this task. 
  Is there any evident advantage/disadvantage of one approach against the others? </p>
  
  <p>Thank you</p>