Coding

ML Model Eval Benchmark

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Benchmark 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

  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.

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