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.