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negative r-squares

Pinar, Jason,
is being made.

In this case R2 can become negative because you use a different
test and train set. Suppose the test set contains one single
extreme that is not present in the training set. In that case, the
mean of the test values is, in terms of sum of squares, a better
predicter than your regression model that didn't know about this
outlier. Don't forget that the mean of the test set does contain
this outlier. Hence, R2 can easily become negative when evaluated
over a different data set then the regression model was derived from.
On 09/09/2010 06:25 PM, Jason Gasper wrote: