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Now, we just measured the error of this linear model against our original training data. We know, though, from say, k and n, that we can build models that can fit this training data exactly. So we can have arbitrarily small error against our training set. The more important measure is, what is our error out of sample? So, what out of sample means is we train on our training set, but we test on a separate testing set of data. And, thats going to be different than our training set. So, to measure out of sample error, we look at the error from our testing set, not our training set. So we look at each one of these test points and measure the error for each one of those. So we look at these blue points instead of the green points, plug them into this equation just like before, and thats our out of sample root mean squared error.
