Individual skill
ml-experiment-evaluation
Plan evaluation strategies for machine-learning product changes. Use when deciding between offline evaluation, interleaving, online A/B tests, multi-armed bandits, or model filtering for ranking, recommendation, search, personalization, or other ML-powered user experiences.
plugins/LVTD-LLC/skills/skills/ml-experiment-evaluation/SKILL.md
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SKILL.mdcore · 3,201 bytesguidelines.mddoc · 1,786 bytesreferences/core/examples.mddoc · 2,389 bytesreferences/core/knowledge.mddoc · 3,271 bytesreferences/core/rules.mddoc · 2,470 bytesworkflows/choose-ml-evaluation-strategy.mddoc · 2,610 bytes
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# ML Experiment Evaluation
Use this skill to choose how to evaluate machine-learning product changes before
they consume live experiment traffic or affect users. It focuses on offline
evaluation, offline-online correlation, interleaving, model filtering, and when
classic A/B testing or adaptive strategies are justified.
## Source Traceability
Primary source: *Next-Level A/B Testing* by Leemay Nassery. Guidance is
transformed and paraphrased from Chapter 4 on offline evaluation, offline-online
correlation, multi-armed bandits, and interleaving for rankers.
Related skills: