In the previous chapter, you learned how to predict the real values using linear regression. In this chapter, you will learn about classification. Classification is the process of tagging/marking a given object with a class/tag value. For example, for a given set of cancer patient records with benign and malignant cancerous cases, a program can be written to automatically categorize the new patient record to be either benign or malignant.

What's fascinating about this approach is that the algorithm doesn't change and you can use the same algorithm to predict other things that are important in other settings. For example, the same algorithm can tell apart dogs and cats from their photographs, as you will find out later in the chapter.

In this chapter, you will learn how to implement several algorithms in F# that are used for classification. For some simple algorithms, such as k-NN, you will develop the algorithm from scratch...