Data Quality
missing-streaming
Identify future information leaking into your dataset before wasting weeks training an invalid model
⚡ Use it when: You build aggregated features, work with temporal data, or your training accuracy is suspiciously high
Leakage = using information that wouldn't exist at real prediction time. It's not overfitting — it's contaminating your training set with future signals you won't have in production.
Normalizing with statistics from the entire dataset, including post-event variables as features, using random splits on temporal data, computing aggregates without a strict temporal cutoff.
Strict temporal validation, a feature pipeline without contamination, and models that actually work in production without dramatic performance drops.