title: Multi-target regression using scikit-learn
body: <p>I am solving the classic regression problem using the python language and the scikit-learn library. It's simple:</p>
  
  <pre><code>        ml_model = GradientBoostingRegressor()
          ml_params = {}
          ml_model.fit(X_train, y_train)
  </code></pre>
  
  <p>where <code>y_train</code> is one-dimensional array-like object</p>
  
  <p>Now I would like to expand the functionality of the task, to get not a single target value, but a set of them. Training set of samples <code>X_train</code> will remain the same. 
  An intuitive solution to the problem is to train several models, where <code>X_train</code> for all of them will be the same but <code>y_train</code> for each model will be specific. This is definitely a working, but, it seems to me, inefficient solution.</p>
  
  <p>When searching for alternatives, I came across such concepts as Multi-Target Regression. As I understand such functionality is not implemented in scikit-learn. 
  How to solve Multi-Target Regression problem in python in efficient way? Thanks)</p>