NannyML/The-Little-Book-of-ML-Metrics
The book every data scientist needs on their desk.
What it solves
This project provides a comprehensive, accessible reference guide for machine learning metrics, addressing the common gap in formal education where these metrics are often overlooked or poorly explained.
How it works
The book serves as a handbook for data scientists, offering clear and concise explanations of metrics across various domains—ranging from standard regression and classification to specialized areas like GenAI, bias and fairness, and data observability. It is maintained as an open-source project, allowing the community to contribute to its content.
Who it’s for
It is designed for both aspiring data scientists and experienced professionals who need a reliable, quick-reference guide to understand and apply machine learning metrics effectively in their work.
Highlights
- Covers a wide range of categories including Regression, Classification, Clustering, Ranking, Computer Vision, NLP, and GenAI.
- Includes specialized sections on Bias and Fairness and Data Observability.
- Available as a free digital version with a high-quality printed edition available for purchase.
- Encourages community involvement, offering contributors and reviewers affiliate links and recognition in the book.
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