Last updated: January 26, 2026 by the editorial team
Author's): Rohan Mistry
Originally published in Towards Artificial Intelligence.
Understand data leaks, target leaks, and temporal leaks – and how to detect and prevent them
Your machine learning model achieves 98% accuracy on validation data. Your team is celebrating. You deploy to production.
The article dives deeper into the concept of data leakage in machine learning, explaining how it can lead to models performing well on training data but failing in real-world applications. It discusses the three main types of leaks – function leak, target leak, and timing leak – with examples from fields such as finance and healthcare. The importance of detecting and preventing these problems is highlighted, along with various strategies such as feature importance analysis and domain knowledge review, which are key to ensuring that models are truly predictive and not deceptively accurate.
Read the entire blog for free on Medium.
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