Summary:
- The article explains the phenomenon of "data leakage" in machine learning, where information from the test set inadvertently influences the model training process.
- It highlights how this error leads to artificially inflated performance metrics, causing models to appear more accurate than they are in real-world, unseen data scenarios.
- The author provides practical guidance on identifying and mitigating leakage, such as ensuring strict temporal splits in time-series data and preventing feature contamination.