# data leakage

*Entry of the [Dictionary of Applied Machine Learning](https://dictionaryofml.org) (course edition), by Alexander Jung, Aalto University. CC BY 4.0. Updated 2026-10-29. [HTML](https://dictionaryofml.org/terms/dataleakage.html) · [typeset PDF](https://dictionaryofml.org/terms/dataleakage.pdf) · [LaTeX](https://dictionaryofml.org/terms/dataleakage.tex) · [Python demo](https://dictionaryofml.org/terms/dataleakage.py) · [notebook](https://dictionaryofml.org/terms/dataleakage.ipynb)*

Math is LaTeX with the book's macros expanded, so no notation package is needed to read it.

## Abstract

Data leakage arises when a machine learning method uses features that are available during training but not at prediction time. For example, in weather forecasting each data point covers a time interval, and one feature is the temperature one hour ahead of the forecast time. On historic data this later measurement is available, but when a forecast is actually computed it is not, so the feature leaks information. A leaked feature makes the loss on the training set and test set look near zero, yet the learned hypothesis fails after deployment, because the feature cannot be reproduced at prediction time. The term is also used, in a security sense, for the unintentional release of sensitive information.

## Definition

Consider a machine learning method trained on an hourly temperature sequence, where each data point is a sliding window of three consecutive hours (Fig. 1). The label is the temperature of the middle hour, and one of the features is the measurement one hour later. On historic data this later measurement is available, so the loss on the training set and test set looks near zero; but in real time the middle hour must be predicted before the next hour is measured, so the learned hypothesis is useless. This is data leakage: a feature used during training or evaluation is unavailable when a prediction must be delivered.

![Figure 1 of the entry dataleakage](https://dictionaryofml.org/terms/fig/dataleakage-fig1.svg)

*Figure 1: Hourly temperature measurements. Each data point is a sliding window of three hours (box); the label is the middle hour's temperature. Using the measurement one hour later as a feature leaks information: at prediction time (dashed line) that hour has not been measured yet, so the feature is available for historic training data only*

More generally, a leaked feature is one whose value cannot be reproduced at prediction time, whether it is measured only later (as above) or computed from the label. Data leakage is formalized by Kaufman et al. (2012), who also give methods to detect and avoid it. Kapoor and Narayanan (2023) survey it as a cause of the reproducibility crisis in machine learning-based science. Test set contamination, the presence of test set data points in the training set, is a related form of data leakage: there, the information that should be unavailable at training time is the test set itself.

The term *data leakage* is also used, in an unrelated security sense, for the unintentional release of sensitive information (data exfiltration from a trained system), a meaning distinct from the methodological one defined above.

**See also:** test set contamination, [training set](https://dictionaryofml.org/terms/trainset.html), [validation set](https://dictionaryofml.org/terms/valset.html), [test set](https://dictionaryofml.org/terms/testset.html), [generalization](https://dictionaryofml.org/terms/generalization.html), risk.

## References

- Kaufman et al. (2012). Leakage in Data Mining: Formulation, Detection, and Avoidance. *ACM Transactions on Knowledge Discovery from Data*. <https://doi.org/10.1145/2382577.2382579>
- Kapoor and Narayanan (2023). Leakage and the Reproducibility Crisis in Machine-Learning-Based Science. *Patterns*. <https://doi.org/10.1016/j.patter.2023.100804>

## Cite this entry

```bibtex
@misc{dictml_dataleakage,
  author = {Jung, Alexander},
  editor = {Olioumtsevits, Konstantina and Schnoor, Ekkehard},
  title = {data leakage},
  howpublished = {Dictionary of Applied Machine Learning (course edition)},
  year = {2026},
  doi = {10.5281/zenodo.21569296},
  note = {ISBN 978-952-64-3013-3, CC BY 4.0, retrieved 2026-11-11},
  url = {https://dictionaryofml.org/terms/dataleakage.html}
}
```