AI agent skill · by @aiming-lab

Experimental Design

Best practices for designing reproducible ML experiments. Use when planning ablations, baselines, or controlled experiments.

Archived 2026-10-23 · Source updated 2026-08-19 · License: MIT. Check the original repository for the latest version.

SKILL.md instructions

## Experimental Design Best Practice
1. ALWAYS include meaningful baselines (not just random):
   - At least one classical method baseline
   - At least one recent SOTA method baseline
   - A simple-but-strong baseline (e.g., linear probe, k-NN)
2. Use MULTIPLE random seeds (minimum 3, ideally 5)
3. Report mean +/- std across seeds
4. Design ablations that isolate EACH key component:
   - Remove one component at a time
   - Each ablation must be meaningfully different from baseline
5. Control variables: change only ONE thing per comparison
6. Use standard splits (train/val/test) — never test on training data
7. Report wall-clock time and memory usage alongside accuracy