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ML Model Eval Benchmark
Try itBenchmark model metrics with clear ranking
What it does
Produce consistent model ranking outputs from metric-weighted evaluation inputs.
The skill document
ML Model Eval Benchmark
Overview
Produce consistent model ranking outputs from metric-weighted evaluation inputs.
Workflow
- Define metric weights and accepted metric ranges.
- Ingest model metrics for each candidate.
- Compute weighted score and ranking.
- Export leaderboard and promotion recommendation.
Use Bundled Resources
- Run
scripts/benchmark_models.pyto generate benchmark outputs. - Read
references/benchmarking-guide.mdfor weighting and tie-break guidance.
Guardrails
- Keep metric names and scales consistent across candidates.
- Record weighting assumptions in the output.
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