Target Leakage
A problem in machine learning modeling in which training features incorporate information that is only available after the prediction time point or is directly related to the outcome, causing validation performance to be inflated and the model to fail after deployment.
Timeline (last 90 days)
Target leakage occurs when a feature contains information about the target that should not legitimately be available at prediction time, or when information about the target or the evaluation process enters training in an invalid way, leading to misleading model evaluation.
Data Autopsy's target leakage check looks for several suspicious signals, including features identical to the target, very strong numeric relationships, unusually strong categorical class purity, and, where applicable, high ROC-AUC scores, but these signals alone cannot prove leakage.
All Facts (2)
Target leakage occurs when a feature contains information about the target that should not legitimately be available at prediction time, or when information about the target or the evaluation process enters training in an invalid way, leading to misleading model evaluation.
50%UnverifiedData Autopsy's target leakage check looks for several suspicious signals, including features identical to the target, very strong numeric relationships, unusually strong categorical class purity, and, where applicable, high ROC-AUC scores, but these signals alone cannot prove leakage.
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