Individual skill
ml-benchmark-evaluation
Rigorous methodology for evaluating ML models on established benchmarks. Covers proper train/val/test splits, baseline verification from original papers, exact metric formula discrepancies, data-leak detection checklist, multi-seed robustness, and honest reporting templates. Use when claiming to beat published baselines, writing methods papers, or auditing existing results.
backend/cli/skills/ml-training/ml-benchmark-evaluation/SKILL.md
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- agent Repository topic
- ai Repository topic
- bun Repository topic
- cli Repository topic
- llm Repository topic
- ml Repository topic
- open-source Repository topic
- research Repository topic
- science Repository topic
- ai-agent Repository topic
- evaluation Declared skill tag
- benchmark Declared skill tag
- validation Declared skill tag
- train-val-test Declared skill tag
- metrics Declared skill tag
- data-leak Declared skill tag
- reproducibility Declared skill tag
- writing Derived category
- data Derived category
- testing Derived category
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# ML Benchmark Evaluation — Rigorous Methodology
## When to Use
- Claiming to beat published baselines on any ML benchmark
- Writing a methods paper that compares against prior work
- Any ML evaluation where the reported number matters for publication
- Auditing existing results for overfitting or data leakage
## The Golden Rule
**Your evaluation protocol must be AT LEAST as rigorous as the baseline you claim to beat. Ideally, stricter.**
## 1. Train/Val/Test Split
