title: GridSearch over MultiOutputRegressor?
body: <p>Let's consider a multivariate regression problem (2 response variables: Latitude and Longitude). Currently, a few machine learning model implementations like Support Vector Regression <code>sklearn.svm.SVR</code> do not currently provide naive support of multivariate regression. For this reason, <code>sklearn.multioutput.MultiOutputRegressor</code> can be used.</p>
  
  <p><strong>Example:</strong></p>
  
  <pre><code>from sklearn.multioutput import MultiOutputRegressor
  svr_multi = MultiOutputRegressor(SVR(),n_jobs=-1)
  
  #Fit the algorithm on the data
  svr_multi.fit(X_train, y_train)
  y_pred= svr_multi.predict(X_test)
  </code></pre>
  
  <p>My goal is to tune the parameters of <code>SVR</code> by <code>sklearn.model_selection.GridSearchCV</code>. Ideally, if the response was a single variable and not multiple, I would perform an operation as follows:</p>
  
  <pre><code>from sklearn.svm import SVR
  from sklearn.model_selection import GridSearchCV
  from sklearn.pipeline import Pipeline
  
  pipe_svr = (Pipeline([('scl', StandardScaler()),
                    ('reg', SVR())]))
  
  grid_param_svr = {
      'reg__C': [0.01,0.1,1,10],
      'reg__epsilon': [0.1,0.2,0.3],
      'degree': [2,3,4]
  }
  
  gs_svr = (GridSearchCV(estimator=pipe_svr, 
                    param_grid=grid_param_svr, 
                    cv=10,
                    scoring = 'neg_mean_squared_error',
                    n_jobs = -1))
  
  gs_svr = gs_svr.fit(X_train,y_train)
  </code></pre>
  
  <p>However, as my response <code>y_train</code> is 2-dimensional, I need to use the <code>MultiOutputRegressor</code> on top of SVR. How can I modify the above code to enable this <em>GridSearchCV</em> operation? If not possible, is there a better alternative?</p>