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---
name: ml-model-eval-benchmark
description: "Compare model candidates using weighted metrics and deterministic ranking outputs. Use for benchmark leaderboards and model promotion decisions."
---
# ML Model Eval Benchmark
## Overview
Produce consistent model ranking outputs from metric-weighted evaluation inputs.
## Workflow
1. Define metric weights and accepted metric ranges.
2. Ingest model metrics for each candidate.
3. Compute weighted score and ranking.
4. Export leaderboard and promotion recommendation.
## Use Bundled Resources
- Run `scripts/benchmark_models.py` to generate benchmark outputs.
- Read `references/benchmarking-guide.md` for weighting and tie-break guidance.
## Guardrails
- Keep metric names and scales consistent across candidates.
- Record weighting assumptions in the output.
