Neural Network Lexicon

Reproducibility in ML

Short Definition

Reproducibility in machine learning is the ability to reliably recreate experimental results.

Definition

Reproducibility in ML refers to the capacity for independent researchers or practitioners to obtain the same results using the same data, code, and evaluation procedures. It requires precise documentation of datasets, preprocessing steps, model configurations, training procedures, and evaluation protocols.

Reproducibility ensures that results are verifiable rather than anecdotal.

Why It Matters

Without reproducibility, performance claims cannot be trusted or compared. Irreproducible results slow scientific progress, obscure failure modes, and undermine confidence in both research and deployed systems.

Reproducibility is a prerequisite for credibility.

Levels of Reproducibility

Reproducibility can be considered at multiple levels:

  • Result reproducibility: same outcomes using the same setup
  • Method reproducibility: same conclusions using independently implemented methods
  • Conceptual reproducibility: same findings across datasets or conditions

Most benchmarks target result reproducibility, not conceptual robustness.

Key Factors Affecting Reproducibility

Common sources of irreproducibility include:

  • undocumented preprocessing steps
  • random seeds not controlled or reported
  • hardware and numerical differences
  • nondeterministic training operations
  • ambiguous evaluation protocols
  • evolving datasets or dependencies

Small omissions can produce large discrepancies.

Reproducibility vs Replicability

  • Reproducibility: same data and code yield the same results
  • Replicability: independent data and implementations support the same conclusions

Both are important, but they answer different questions.

Minimal Conceptual Example

# conceptual reproducibility checklist
set_random_seed()
freeze_dependencies()
log_hyperparameters()
document_protocol()

Reproducibility in Practice

Good reproducibility practices include:

  • fixing random seeds and logging randomness sources
  • versioning data, code, and environments
  • using deterministic evaluation pipelines
  • documenting evaluation protocols clearly
  • reporting variance and uncertainty, not just point estimates

Reproducibility is a process, not a toggle.

Common Pitfalls

  • reporting only best-run results
  • omitting failed experiments
  • changing protocols without disclosure
  • relying on implicit defaults
  • assuming benchmarks guarantee reproducibility

Benchmarks enable reproducibility only if used correctly.

Relationship to Benchmarking

Reproducibility underpins responsible benchmarking. Without reproducible practices, leaderboard comparisons and benchmark improvements lose meaning.

Relationship to Generalization

Reproducible results can still fail to generalize. Reproducibility ensures correctness of evidence; generalization determines real-world relevance.

Related Concepts

  • Generalization & Evaluation
  • Benchmarking Practices
  • Evaluation Protocols
  • Benchmark Leakage
  • Hidden Test Sets
  • Statistical Significance