title: scikit-learn classification on soft labels
body: <p>According to the documentation it is possible to specify different loss functions to <code>SGDClassifier</code>. And as far as I understand <code>log loss</code> is a <code>cross-entropy</code> loss function which theoretically can handle soft labels, i.e. labels given as some probabilities [0,1].</p>
  
  <p>The question is: is it possible to use <code>SGDClassifier</code> with <code>log loss</code> function out the box for classification problems with soft labels? And if not - how this task (linear classification on soft labels) can be solved using scikit-learn?</p>
  
  <p><strong>UPDATE:</strong></p>
  
  <p>The way <code>target</code> is labeled and by the nature of the problem hard labels don't give good results. But it is still a classification problem (not regression) and I wan't to keep probabilistic interpretation of the <code>prediction</code> so regression doesn't work out of the box too. Cross-entropy loss function can handle soft labels in <code>target</code> naturally. It seems that all loss functions for linear classifiers in scikit-learn can only handle hard labels.</p>
  
  <p>So the question is probably:</p>
  
  <p>How to specify my own loss function for <code>SGDClassifier</code>, for example. It seems <code>scikit-learn</code> doesn't stick to the modular approach here and changes need to be done somewhere inside it's sources</p>