This blog post on classification algorithms is part of the article series Understanding AI Algorithms. Classification algorithms, also known as classifiers, are used to put observations into defined groups.
For example, it is known that some customers respond well to marketing emails, and some do not.
Wouldn’t it be great to know which customers will respond before sending the emails so you can target the right people with the best personalization?
This is a typical problem that can be solved with a classification algorithm. The algorithms explained in this section all handle multi-class problems, meaning they can classify data into more than two groups.
For example, they can be used to classify customers as low spenders, medium spenders, or high spenders, and then to study what characteristics affect how much they spend.
This blog post lists four different algorithms that can be used for the classification of data. They are appropriate when seeking information about what kind of customers are most likely to respond on an offer, or to examine what strategy works best for a specific market.
We’ll begin with one of the simplest to understand and move on to more complex models as we go:
If you want to read all the related articles on the topic of AI algorithms, here is the list of all blog posts in this article series:
