SkillHub

ml-model-eval-benchmark

v0.1.0

Compare model candidates using weighted metrics and deterministic ranking outputs. Use for benchmark leaderboards and model promotion decisions.

Sourced from ClawHub, Authored by Muhammad Mazhar Saeed

Installation

Please help me install the skill `ml-model-eval-benchmark` from SkillHub official store. npx skills add 0x-Professor/ml-model-eval-benchmark

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.