enforcing-leakage-firewall

Data leakage occurs when supervised model evaluation hides duplicate rows or target-derived signals across splits. It often happens after joins or merges in multi-source crosswalks, causing suspiciously high held-out metrics that fail in production.

You validate grouped entity data by applying group-aware splitting and no-row-in-two-splits hashing. This prevents repeated customers, patients, or shared entities from appearing in both training and test sets, ensuring your held-out metric is trustworthy.

You need leave-one-source-out hub-firewall validation when merging multi-source crosswalks or recommender histories. It exposes cross-source leakage by holding out an entire data source during training, verifying that performance is not driven by source-specific signals.

Random split evaluation hides duplicate rows in merged registries by distributing repeated entities across training and test sets. This inflates held-out metrics, requiring group-aware splitting and no-row-in-two-splits hashing to detect and block the leakage.

You run leave-one-feature-out sweeps by iteratively removing individual features and re-evaluating the model. This identifies target-derived features that artificially inflate performance, helping you isolate suspicious post-join signals before trusting your metric.

Group-aware splitting prevents entity overlap across splits but does not detect target-derived features or cross-source leakage. You must combine it with leave-one-feature-out sweeps and leave-one-source-out validation for comprehensive data leakage detection.