Experimental Design

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

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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

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