NannyML/The-Little-Book-of-ML-Metrics
A comprehensive open-source reference book cataloging machine learning evaluation metrics across regression, classification, clustering, ranking, NLP, computer vision, and GenAI domains.
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The Little Book of ML Metrics provides clear, concise explanations of machine learning metrics from accuracy to obscure ones like the P4 metric. Written to fill gaps in traditional data science education, it serves as a quick-reference handbook covering 10 metric categories including regression, classification, clustering, ranking, computer vision, NLP, probabilistic metrics, bias/fairness, and data observability. The repository is open-source with a purchasable printed edition supporting ongoing development.
Frequently asked
- What is NannyML/The-Little-Book-of-ML-Metrics?
- A comprehensive open-source reference book cataloging machine learning evaluation metrics across regression, classification, clustering, ranking, NLP, computer vision, and GenAI domains.
- Is The-Little-Book-of-ML-Metrics open source?
- Yes — NannyML/The-Little-Book-of-ML-Metrics is an open-source project tracked on heatdrop.
- What language is The-Little-Book-of-ML-Metrics written in?
- NannyML/The-Little-Book-of-ML-Metrics is primarily written in Jupyter Notebook.
- How popular is The-Little-Book-of-ML-Metrics?
- NannyML/The-Little-Book-of-ML-Metrics has 998 stars on GitHub.
- Where can I find The-Little-Book-of-ML-Metrics?
- NannyML/The-Little-Book-of-ML-Metrics is on GitHub at https://github.com/NannyML/The-Little-Book-of-ML-Metrics.