The ultimate guide to binary classification metrics

Choosing a proper metric is a crucial yet difficult part of the machine learning project. In this blog post, you will learn about a number of common and lesser-known metrics and performance charts as well as typical decisions when it comes to choosing one for your project. I really wanted to make this post complete(ish) and covered a lot. Also, I wanted each metric section to contain everything you need (repeating some things here and there) so that you can just jump to the metric you are interested in and read that section alone. I know it is a lot to go over but if you want to make your guide "ultimate" you really have to go for it . I think it usually easier to understand things, like classification metrics in the context of something palpable.