Data Leakage

Data leakage refers to the unintentional exposure of sensitive or confidential information to unauthorized parties. This can occur through various means, including inadequate data protection measures, misconfigured security settings, or human error. In the context of machine learning and data analysis, data leakage specifically refers to situations where information from the testing dataset is inadvertently included in the training dataset, leading to overly optimistic performance metrics and poor generalization to unseen data. It undermines the model's ability to make accurate predictions in real-world scenarios, as it has "seen" information it wouldn't have access to in practice. Preventing data leakage is crucial for maintaining data integrity and the reliability of analytical outcomes.