What is meant by 'data leakage' in model training?

Data leakage refers to the inappropriate use of outside information when creating a predictive model, which can lead to overly optimistic performance metrics. In practical terms, this occurs when a model is exposed to information from the test dataset during the training phase, or when features are selected that will not be available in a real-world application. This undermines the validity of the model, as it may perform well on the training data but poorly on unseen data, leading to misleading conclusions about its applicability and accuracy.

Effective model training depends on the principle that the model should only learn from the data that is supposed to be available at the time of prediction. If information from the future (or from the test set) influences the training process, the model is not truly learning to predict outcomes based on the inputs—it is essentially memorizing patterns that would not exist in practical scenarios. Ensuring that data leakage does not occur is crucial for building reliable and generalizable models.

The other options, while related to data issues, do not capture the essence of data leakage: excess data causing performance issues relates to the volume of data, incorrectly labeled data refers to errors in data quality, and unintentional omissions deal with incomplete datasets, all of which are important but distinct from the concept of