Label leakage occurs when training data contains information that would not be available at prediction time but is directly or indirectly related to the target label. It can make model performance appear unrealistically high during evaluation while failing in real deployment.
A model design flaw in which a feature is a proxy for the label. For example, consider a binary classification model that predicts whether or not a prospective customer will purchase a particular product. Suppose that one of the features for the model is a Boolean named . Further suppose that a customer agent is only assigned after the prospective customer has actually purchased the product. During training, the model will quickly learn the association between and the label. See Monitoring pipelines in Machine Learning Crash Course for more information.