In this chapter, you were introduced to TDD, as well as BDD. With these concepts introduced, you have a basic foundation with which to approach machine learning. We saw that specifying behavior in the form of sentences makes it easier to ready a set of specifications for your software.

Building off of that foundation, we started to delve into testing at a higher level. We did this by establishing concepts that we can use to quantify classifiers: the ROC curve and AUC metric. Now we've seen that different models can be quantified, it follows that they can be compared.

Putting all of this together, we have everything we need to explore machine learning with a test-driven methodology. In the next chapter, we will build a simple perceptron algorithm with TDD and measure its quality.