How does EDA help catch target leakage?

medium

Answer

$Look for features with suspicious high correlation to target: (1) any feature perfectly predictive by itself is suspect; (2) features generated after the target event should not exist at prediction time; (3) IDs / timestamps sometimes carry target information indirectly.$
$Cross-checks: check feature availability at inference time (would you have this feature when predicting?); check for target-derived aggregations (e.g., 'average order value' when predicting a churn outcome computed after the churn).$

Check yourself — multiple choice

EDA can't catch leakage
Suspiciously high correlation + features that only exist post-outcome + target-derived aggregations — audit availability at inference time
Random search
Only classification

EDA leakage checks: high correlation + post-outcome features + target-derived aggregations.

#eda

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