This guide uses testable rule assumptions and does not infer any platform’s implementation from its name. It is educational material, not betting advice.
Key definition
Repeated tuning until old data looks best learns accidental noise as if it were a rule: overfitting.
How to verify it
Features containing future information, or preprocessing fitted on the test period, create data leakage.
Limits and conclusion
Changes in rules, shuffling, logging or user behavior create distribution shift.
Practical checklist
Keep a final lockbox set, record every experiment and report continuous live results rather than the best segment.
Practical checklist
- Repeated tuning until old data looks best learns accidental noise as if it were a rule: overfitting.
- Changes in rules, shuffling, logging or user behavior create distribution shift.
- Fix the rules, preserve a complete record, separate training from testing, and only then ask whether a model adds information beyond a simple baseline.