What is data leakage, and how do you prevent it?

Data leakage is when information unavailable at prediction time influences training. It produces excellent validation scores and a model that fails in production, and it is the single most common serious mistake in applied machine learning.

The main forms:

• Target leakage — a feature that is a consequence of the outcome. Including "number of collection calls" when predicting default, or "discharge medication" when predicting diagnosis. The giveaway is suspiciously high performance and one feature dominating importance.
• Train-test contamination — fitting a scaler, imputer, or encoder on the full dataset before splitting. The transformer has then seen the test data's distribution.
• Temporal leakage — training on data from after the prediction point. Random splits on time series always do this.
• Group leakage — the same entity appearing in both train and test, so the model memorises rather than generalises.
• Duplicate rows spread across splits.

How to prevent it:

• Split first, then fit everything on the training split only. Use a scikit-learn Pipeline so preprocessing is fitted inside each cross-validation fold automatically — this eliminates the most common form structurally.
• Split by time for temporal problems, and by group where entities repeat.
• Interrogate every feature: would this value exist, with this content, at the moment of prediction?
• Treat implausibly good results as a bug report, not a success.