Data leakage, also called target leakage, is a problem affecting several stages of the machine learning life-cycle, from data collection to model evaluation.
Data leakage in supervised learning is the unintentional introduction of information about the target that should not be made available. We call it “contamination”. Training on contaminated data leads to overly optimistic expectations about the model performance.
Three most frequent Causes of Data Leakage that can happen during data collection and preparation:
- Target being a a function of a feature
- Feature hiding the target
- Feature coming from the future
Data Imputation