# Very low MSE, but negative R^2 [different case than similar questions]

This question has been asked before, but I have a specific case I'm curious about. Essentially, I have a very low MSE, but the R^2 turns out to be negative. Even the scatterplot shows that a horizontal line isn't a good fit, so I'm not sure what to make of these results. I used sklearn's Linear Regression object, and calculated "Variance Score" (R^2) with the .score() function with .score(Xtrain, ytrain) for the training set (leftmost plot) and .score(Xtest, ytest) for the testing set (middle plot). These are time series predictions btw Thanks]1 []2

• Welcome to Cross Validated! Please take a moment to view our tour. It looks like you end up with a combinations of two distributions (or a bi-modal distribution) which is especially prevalent in the second model. Mar 13, 2017 at 19:35

The $R^2$ value indicates that a null model (horizontal line) is a better fit for the test data than the model you've created in the training data. If you plot the $x=y$ line in your test set graphs, you can see that this line lies almost entirely above the predicted y values. For the null model, roughly half of the predicted values will lie above this line and half will lie below it, even though the predictions are all the same.
The small $MSE$ doesn't indicate much here. The null model would also have a small $MSE$.