title: regr.score and r2_score give different values
body: <p>I am using sklearn.linear_model.LogisticRegression and using that I calculate the R^2 value as follows</p>
  
  <p><code>regr.score(xtest, ytest)</code> </p>
  
  <p>and I get a score of 0.65</p>
  
  <p>Now, just to compare i used the metric provided by sklearn.metrics.r2_score¶ and I calculate the score as follows</p>
  
  <p><code>r2_score(ytest,regr.predict(xtest))</code></p>
  
  <p>and I get a score of -0.54</p>
  
  <p>According to the documentation regr.score returns "R^2 of self.predict(X) wrt. y." and this is what I did to calculate R^2 using the metric, but I don't get why the values are so different?</p>
  
  <p>Can anyone help me explain it a bit?</p>
  
  <p><strong>Update:</strong> As suggested I switched the variables ytest,regr.predict(xtest) in r2_score, but in logistic regression I still get different values. So I updated the question.</p>