NannyML/The-Little-Book-of-ML-Metrics resource

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Adoption not part of the score

1003 stars · 83 forks observed · 2026-09-03

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

An open-source reference book covering machine learning evaluation metrics across regression, classification, clustering, ranking, computer vision, NLP, GenAI, bias and fairness, and data observability. It is maintained by the NannyML team as a quick-reference handbook for data scientists, with a free digital version and a paid printed edition.

Use cases

• look up the definition of a regression metric like MAPE
• find the right classification metric for an imbalanced dataset
• learn evaluation metrics for ranking or recommender systems
• understand computer vision metrics like IoU or mAP
• compare NLP and GenAI evaluation metrics
• find metrics for bias and fairness auditing
• quick reference handbook for ML model evaluation

When to choose

• you need a concise reference explaining ML evaluation metrics
• you want free, open-source educational material on metrics
• you are a data scientist or ML engineer evaluating models across domains
• you want to contribute to or review an open-source metrics book

When to avoid

• you need a software library to compute metrics (use scikit-learn or NannyML instead)
• you need ML model monitoring tooling rather than metric documentation
• you need a formal textbook with exercises rather than a quick-reference handbook

Facets

learning-resource · maturity active

machine-learning data-science documentation machine-learning data-science tutorials education cross-platform book ml-metrics evaluation-metrics reference-handbook open-source-book jupyter-notebook

Member repositories

For agents

markdown · JSON · MCP: product_card(name="NannyML/The-Little-Book-of-ML-Metrics")