title: Regression with multi-dimensional targets
body: <p>I am using <em>scikit-learn</em> to do regression and my problem is the following. I need to do regression on several parameters (vectors). This works fine with some regression approaches such as <code>ensemble.ExtraTreesRegressor</code> and <code>ensemble.RandomForestRegressor</code>. Indeed, one can give a vector of vectors as targets to fit the model (<code>fit(X,y)</code> method) for the two aforementionned regression methods.</p>
  
  <p>However when I try with <code>ensemble.GradientBoostingRegressor</code>, <code>ensemble.AdaBoostRegressor</code> and <code>linear_model.SGDRegressor</code>, the classifier fails to fit the model because it expects 1-dimensional values as targets (y argument of the <code>fit(X,y)</code> method). This means, with those Regression methods I can estimate only one parameter at a time. This is not suitable for my problem because it might take some time while I need to estimate about 20 parameters. On the other hand, I really would like to test those approaches.</p>
  
  <p>So, my question is: Does anyone know if there is a solution to fit the model once and estimate several parameters for <code>ensemble.GradientBoostingRegressor</code>, <code>ensemble.AdaBoostRegressor</code> and <code>linear_model.SGDRegressor</code>?</p>
  
  <p>I hope I've been clear enough ... </p>