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Test-Driven Machine Learning by Justin Bozonier

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Summary

In this chapter, we built up a Gaussian Naïve Bayes classifier, and ran into our first examples of truly necessary refactoring. We also saw how needing to make enormous changes in the code for a test is sometimes the result of trying to test too many concepts at once. We saw how backing up and rethinking test design can ultimately lead to a better and more elegantly designed piece of software as well.

In the next chapter, we'll apply this classifier to the real data, and see what it looks like to compare how different classifiers perform on the same data.

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