Science✓ Mathematical
◆ The PatternEnsuring any result can be replicated exactly
Reproducibility means anyone can re-run your experiment and get the same result. This requires pinned dependencies, fixed random seeds, versioned data (DVC), containerised environments, and recorded hardware specs. Without it, your "state of the art" result is just a story.
// Interactive — reproducibility checklist
# Python — reproducibility essentials import random, numpy as np, torch # Lock all seeds SEED = 42 random.seed(SEED) np.random.seed(SEED) torch.manual_seed(SEED) torch.cuda.manual_seed_all(SEED) torch.backends.cudnn.deterministic = True # Pin deps: pip freeze > requirements.txt # Version data: dvc add data/train.csv # Container: docker build -t experiment:v1 .
Pattern bridge: Reproducibility in ML is the same discipline as walk-forward validation — if you can't reproduce it, you can't trust it. In markets, backtest reproducibility faces the same challenge with data versioning.