residual sum of squares should be computed by summing the squared differences instead of taking the mean.

Author: KjellSwedin

examples/linear_model/plot_ols.py

@@ -52,7 +52,7 @@
 print('Coefficients: \n', regr.coef_)
 # The mean square error
 print("Residual sum of squares: %.2f"
-      % np.mean((regr.predict(diabetes_X_test) - diabetes_y_test) ** 2))
+      % np.sum((regr.predict(diabetes_X_test) - diabetes_y_test) ** 2))
 # Explained variance score: 1 is perfect prediction
 print('Variance score: %.2f' % regr.score(diabetes_X_test, diabetes_y_test))