I am currently working on a book on ML metrics.

Choosing the right metrics and understanding them is one of the most important parts of data science work. However, I have seen that this is rarely taught in courses or university degrees. Even senior data scientists often have only a basic understanding of metrics.

The idea of ​​the book is to be a little handbook that sits on every data scientist’s desk, as a quick reference of the most well-known metrics, ahem, accuracy, to the most obscure thing (looking at you, P4-metric)

The book covers the following types of statistics:

• Regression
• Classification
• Clustering
• Ranking
• Vision
• Text
• GenAI
• Bias and honesty

Sample page

This is what a complete metric page looks like.

What else would you like to see explained/covered for each metric? Do you have any specific requests?