Building Machine Learning Systems with Python - Richert, Coelho

Getting Started with Python Machine Learning
The models of degree 10 and 100 don't seem to expect a bright future for our
startup. They tried so hard to model the given data correctly that they are clearly
useless to extrapolate further. This is called overfitting. On the other hand, the
lower-degree models do not seem to be capable of capturing the data properly.
This is called underfitting.
So let us play fair to the models of degree 2 and above and try out how they behave
if we fit them only to the data of the last week. After all, we believe that the last
week says more about the future than the data before. The result can be seen in the
following psychedelic chart, which shows even more clearly how bad the problem
of overfitting is:
Still, judging from the errors of the models when trained only on the data from week
3.5 and after, we should still choose the most complex one.
Error d=1: 22143941.107618
Error d=2: 19768846.989176
Error d=3: 19766452.361027
Error d=10: 18949339.348539
Error d=100: 16915159.603877
Training and testing
If only we had some data from the future that we could use to measure our
models against, we should be able to judge our model choice only on the resulting
approximation error.
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