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Test-Driven Machine Learning by Justin Bozonier

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Summary

In this instance, it looks like our home grown Naïve Bayes just slightly outperforms Random Forest with some quick tuning and tweaking. We have a deep understanding of creating Gaussian Naïve Bayes classifiers, and we've seen how little we need to understand Random Forests in order to use them as a black box.

In the next chapter, we're going to explore and further dig into libraries like sklearn. We'll use TDD and our unit test tool as a way to build documentation and learn about the code. We'll continue working with classes, and we'll find new ways of testing that we're using sklearn and other third party libraries the way we think we are.

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