Kaggle Posts Mislead Beginners on Small Data with Unreplicable High Scores (www.kaggle.com)

A Kaggle community post calling out popular Titanic notebooks shows many high-scoring kernels are teaching beginners dangerous habits on very small data. The author reproduced top techniques (20+ engineered features, family-survival “magic” features that leak info, large ensembles) and found they boost cross‑validation (CV) but hurt real leaderboard (LB) performance: a simple 5‑feature Logistic Regression scored CV 0.815 / LB 0.792 (gap 2.3%), a medium model CV 0.828 / LB 0.792 (gap 3.6%), while a complex 19‑feature ensemble hit CV 0.843 but fell to LB 0.785 (gap 5.8%). With only 891 training samples the natural sampling variance is ≈±3.3%, so CV→LB gaps of ~3–4% are expected and apparent “0.83” notebooks may be overfit, lucky on that test split, or the result of many submissions. This matters because Titanic is often a first competition that shapes newcomers’ mental models: more features, bigger ensembles and chasing tiny CV gains on small datasets encourage overfitting and false confidence. Technical takeaways: small datasets demand simple, robust baselines (e.g., Pclass, Sex, Age, Fare, Embarked + logistic regression), careful leak checks (avoid family/test overlap), explicit CV-to-LB gap analysis, and skepticism toward marginal CV improvements. The post urges notebook authors to show baselines, warn about dataset-size limits, and for the community to upvote educational notebooks—normalizing 0.78–0.80 as a strong, generalizable result on Titanic.