Overview

ML Model Eval Benchmark produces consistent model rankings by applying metric weights to candidate evaluation inputs. It supports benchmark leaderboards and promotion decisions with transparent, score-based outputs.

How This Skill Works

Start by defining metric weights and accepted ranges, then ingest each candidate’s metrics. The system computes a weighted score and a deterministic ranking, then exports a leaderboard and a promotion recommendation.

When to Use It

• Benchmark multiple model candidates for a public or internal leaderboard.
• Decide on model promotion by comparing weighted performance across metrics.
• Provide a transparent, auditable evaluation flow for governance and stakeholders.
• Experiment with different metric weights and study their impact on rankings.
• Publish standardized results with both raw metrics and weighted scores for cross-team review.

Quick Start

1. Step 1: Define metric weights and accepted ranges.
2. Step 2: Ingest model metrics and run scripts/benchmark_models.py to generate outputs.
3. Step 3: Review the generated leaderboard, weighted scores, and promotion recommendation.

Best Practices

• Keep metric names and scales consistent across all candidates.
• Document all weighting assumptions and accepted metric ranges in outputs.
• Use the bundled script scripts/benchmark_models.py to generate outputs.
• Verify and align tie-break rules with references/benchmarking-guide.md.
• Publish both raw metrics and weighted scores for full transparency.

Example Use Cases

• A team ranks image-classification models for a public leaderboard to decide winner and finalists.
• A production vendor is selected based on a weighted benchmark that reflects deployment goals.
• Governance teams review benchmark outputs with documented weights and tie-break rules.
• Re-run benchmarks after metric changes to assess impact on rankings and promotions.
• Organizations maintain audit logs of weighting choices for regulatory review.

Frequently Asked Questions