In this chapter, we have seen how k-NN and Naïve Bayes work by programming our own implementation of the algorithms. You have discovered how to perform these analyses in R. We have shown you that it is not optimal to test our classifier with the data it has been trained with. We have seen that the number of neighbors selected in k-NN impacts the performance of the classification and examined different performance measures. In the next chapter, you will learn about decision trees.