Apologies for the late addition to the thread, but Kirk's "Thoughtful
Machine Learning with Python" [1] may be of interest. It's unevenly
edited in some places, and skates over some background issues that I
think its intended audience would want more fully explained, but it
opens with a good look at how to apply generic testing principles to the
specific problems of machine learning, and includes some fully-worked
examples for things like Naive Bayes classifiers.
Cheers,
Greg
[1]
https://www.amazon.com/Thoughtful-Machine-Learning-Python-Test-Driven/dp/1491924136/
--
If you cannot be brave – and it is often hard to be brave – be kind. And if you
can't be kind, at least be useful.
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