title: scikit-learn cross validation, negative values with mean squared error
body: <p>When I use the following code with Data matrix <code>X</code> of size (952,144) and output vector <code>y</code> of size (952), <code>mean_squared_error</code> metric returns negative values, which is unexpected. Do you have any idea?</p>
  
  <pre><code>from sklearn.svm import SVR
  from sklearn import cross_validation as CV
  
  reg = SVR(C=1., epsilon=0.1, kernel='rbf')
  scores = CV.cross_val_score(reg, X, y, cv=10, scoring='mean_squared_error')
  </code></pre>
  
  <p>all values in <code>scores</code> are then negative.</p>