Start by defining data leakage as any information from outside the training data that the model shouldn't have access to, then systematically walk through each form (target, train/test, time-based, preprocessing) with concrete examples and detection/prevention strategies. Emphasize that leakage often leads to overly optimistic validation performance and poor production results, and highlight the importance of pipeline design and rigorous validation.
Pro tip: Frame leakage as a silent killer of ML projects: it often goes unnoticed because validation metrics look great, but production performance craters. Mention that Adobe-scale systems require automated leakage detection in CI/CD pipelines, such as feature importance audits and time-series cross-validation.
Explain that data leakage occurs when a model uses information during training that would not be available at prediction time, leading to inflated validation scores and poor generalization. Highlight that it's a common pitfall in real-world ML systems.
Describe target leakage as when features contain information about the target that wouldn't be known at prediction time (e.g., using 'number of late payments' to predict 'default' when that data is only available after default). Detection: check feature availability timelines and correlation with target. Prevention: remove or delay such features, use causal reasoning.
Explain that contamination happens when test data influences training, e.g., through preprocessing steps like scaling or imputation applied before splitting. Detection: compare distributions and check for duplicate rows across sets. Prevention: always split data first, then fit preprocessing only on training data and apply to test.
Discuss that in time-series or temporal data, using future information to predict the past causes leakage. Detection: ensure chronological ordering and check for features that aggregate future data. Prevention: use time-based splits (e.g., train on past, validate on future) and avoid shuffling; use expanding window cross-validation.
Cover leakage introduced during preprocessing, such as feature selection, target encoding, or dimensionality reduction performed on the entire dataset before splitting. Detection: review pipeline for steps that use global statistics. Prevention: encapsulate all preprocessing within a pipeline that is fit only on training folds, e.g., using scikit-learn's Pipeline and ColumnTransformer.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.