Last updated on January 26, 2026 by Editorial Team
Author(s): Rohan Mistry
Originally published on Towards AI.
Understanding data leakage, target leakage, and temporal leakage – and how to detect and prevent them
Your machine learning model achieves 98% accuracy on validation data. Your team celebrates. You are deployed in production.
The article highlights the concept of data leakage in machine learning, explaining how it can cause models to perform well on training data but fail in real-world applications. It outlines three main types of leakage—facility leakage, target leakage, and temporal leakage, providing examples from sectors such as finance and health care. The importance of detecting and preventing these issues, along with various strategies such as feature importance analysis and domain knowledge review, is emphasized, which are critical to ensuring that models are truly predictive rather than misleadingly accurate.
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Comment: The content represents the views of the contributing authors and not those of AI.
