Leaderboard strategy

Leaderboard strategy

The public leaderboard is a trap dressed as feedback. It scores a small slice of the test data, and chasing it overfits to that slice, dropping you hundreds of ranks when the private leaderboard reveals the rest. The whole strategy is: trust your own validation, use the public LB as one weak signal, and select finals by CV.

Method

1. Make your cross-validation the source of truth. A CV scheme that matches how train and test differ (see cross-validation, kaggle-competition-workflow) is your honest, larger measure of progress. The public LB scores far fewer rows; your CV usually estimates the private LB better. When they disagree, investigate rather than trusting the LB.
2. Establish the CV-to-LB relationship early. Submit the baseline and note the gap and correlation between local CV and public LB. A stable offset (LB tracks CV plus a constant) means the LB is informative; a CV that improves while LB does not (or vice versa) is a warning of a split mismatch or a leak you should understand.
3. Do not tune on the public leaderboard. Every decision made to improve the public score, rather than CV, overfits to that small slice. Probing the LB, selecting features by LB movement, or picking hyperparameters by submission is how a strong CV model becomes a fragile LB-tuned one. Limit submissions to sanity-checks, not a search loop.
4. Watch for a shakeup. When the public LB is small or the metric is noisy, private rankings reorder dramatically at reveal ("the shakeup"). The competitors who survive it trusted robust CV and avoided LB-overfitting; those who climbed the public LB by chasing it fall. Expect a shakeup and build for the private set.
5. Select final submissions by CV, with a hedge. You typically choose two final submissions: pick them by cross-validation score, not public LB rank. A common strategy is one best-CV submission and one safe/robust submission (a solid ensemble), hedging against both variance and your CV being slightly off. Never pick both finals by public LB.
6. Understand the split before trusting anything. Whether the public/ private split is random, by time, or by group changes everything; a time-based test means recent-data CV matters most, a grouped test means grouped CV. Read the competition's description and the data to infer the split (see train-test-discipline).

Boundaries

• This is a competition discipline; in production there is no public leaderboard, but the underlying lesson (trust honest offline validation over a small noisy signal) transfers directly to shipping models (see model-deployment, ab-test-design).
• Robust CV cannot fix a leak or a fundamentally wrong split; if CV and LB both look impossible, suspect leakage in the data or your features (see feature-engineering-tabular, exploratory-data-analysis).
• Reading forums and public notebooks is legitimate and valuable (shared tricks, discovered leaks); ignoring the community is its own strategic error.