AI Computing Power · KAEL Editorial Department
Make machine learning experiments reproducible
The outcome of the research is not just a score, but also steps that others can re-perform. Reproducibility makes model comparisons more credible and reduces personnel handover costs.
Document full context
Save code versions, dependencies, random seeds, parameters and data ranges. Saving only the final model file often fails to explain why it behaved differently from the last experiment.
unified comparison conditions
Compare scenarios using the same data partitioning and evaluation process. If the features, model, and evaluation metrics are modified simultaneously, even if the score improves, it is difficult to know where the improvement comes from.
Explaining fluctuations in results
Run multiple runs of a process where randomness exists, and report a range of results rather than just the best one. Paying attention to whether performance is stable is often more helpful in subsequent engineering decisions than chasing a single peak.
