title: Scikit-learn cross validation scoring for regression
body: <p>How can one use <code>cross_val_score</code> for regression? The default scoring seems to be accuracy, which is not very meaningful for regression. Supposedly I would like to use mean squared error, is it possible to specify that in <code>cross_val_score</code>?</p>
  
  <p>Tried the following two but doesn't work:</p>
  
  <pre><code>scores = cross_validation.cross_val_score(svr, diabetes.data, diabetes.target, cv=5, scoring='mean_squared_error') 
  </code></pre>
  
  <p>and  </p>
  
  <pre><code>scores = cross_validation.cross_val_score(svr, diabetes.data, diabetes.target, cv=5, scoring=metrics.mean_squared_error)
  </code></pre>
  
  <p>The first one generates a list of negative numbers while mean squared error should always be non-negative. The second one complains that:</p>
  
  <pre><code>mean_squared_error() takes exactly 2 arguments (3 given)
  </code></pre>