Data leakage refers to the unintentional exposure of sensitive information or the unintended use of data in a manner that can compromise the integrity of a model's performance during its evaluation. This can occur when data from the test set is improperly used during the training phase, leading to overly optimistic performance metrics and poor generalization to unseen data. Understanding data leakage is crucial for accurate model evaluation and optimization, as it directly affects the reliability of predictive models.
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