ml-evaluation-framework

Enforce statistical evaluation and reporting for machine learning experiments.

Updated Feb 23, 2026

Search Tags:#benchmarking #metrics #evaluation #ablation #confidence-intervals #statistical-significance

Ready to run

npx skills add https://github.com/rishikanthc/ml-superpowers --skill ml-evaluation-framework

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ml-evaluation-framework
Source: https://github.com/rishikanthc/ml-superpowers/tree/main/skills/ml-evaluation-framework
Command: npx skills add https://github.com/rishikanthc/ml-superpowers --skill ml-evaluation-framework

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill enforces rigorous evaluation practices to prevent unsupported claims about model performance by requiring variance estimation, confidence intervals, and fair comparisons before reporting results.

Core Features & Use Cases

Statistical Rigor Enforcement: Requires minimum multiple random seeds, mean ± std reporting, and confidence intervals for any claimed improvement.
Evaluation Checklist: Mandates user-specified metrics, full classification metric suite when applicable, ablations for novel components, and fair baseline comparisons using identical data splits and preprocessing.
Use Case: Use when preparing benchmark reports, publishing model improvements, running ablation studies, or comparing models to ensure conclusions are statistically justified.

Quick Start

Ask the assistant to design an evaluation plan that lists metrics, specifies at least three seeds, defines baselines with identical splits, and outlines ablation experiments.

Frequently Asked Questions about ml-evaluation-framework

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I ensure my machine learning evaluation is statistically valid?▼

To ensure machine learning evaluation is statistically valid, you must run experiments across a minimum of three random seeds, report mean and standard deviation, and use confidence intervals or formal significance tests before claiming any model improvements.

What metrics are required for a rigorous classification evaluation?▼

A rigorous classification evaluation requires a full metric suite rather than a single score, alongside user-specified metrics, variance estimation across multiple seeds, and fair baseline comparisons computed on identical data splits.

How do I design an ablation study that proves a model component works?▼

Designing a valid ablation study requires isolating novel components, comparing them against fair baselines using identical data preprocessing, and validating any reported performance differences with confidence intervals or statistical significance tests.

Can I report a benchmark improvement without running multiple random seeds?▼

Reporting benchmark improvements without multiple random seeds is invalid. You must execute at least three seeds, calculate variance, and apply formal significance testing or confidence intervals to justify any claimed performance gains.

What is statistical significance testing used for in ML benchmarking?▼

Statistical significance testing in ML benchmarking is used to verify that performance differences between models are genuine and not random noise, requiring fair baseline reproductions on identical data splits and multi-seed variance estimation.

When should I use confidence intervals instead of just reporting mean accuracy?▼

You should use confidence intervals instead of just mean accuracy whenever preparing benchmark reports or publishing model improvements, as they quantify the uncertainty of your estimates across multiple random seeds.