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How can I tell if my MSE is "good" or "bad"?

Unfortunately, there is no definite answer, as it depends heavily on the
**context** of the problem. In certain situations, like training a model to automate
a car, a high MSE would be disastrous. In other cases, it might be more forgivable.

It isn't *always* good to have a small MSE. If it is too close to 0, we might
see **overfitting**. This means our model is too "tailored" for our specific
data points and might be way off if we tried a new set of similar data points.
Generally, the larger our range of data and the more diverse our data is, the more
"OK" it is to have a large MSE.

The **Moore's Law** example above demonstrates that a model that is pretty
close to the data points might still produce a big MSE, simply because we
are working with fairly large numbers. There is no universal rule for what
counts as a "good" MSE, but we can probably say that an MSE of 100 would be much more
reasonable here than if we were looking at a data set with a range of 10.