Data leakage occurs when machine learning models are trained on a dataset and then tested on the same data, causing them to memorize answers rather than learn underlying patterns, which results in artificially perfect accuracy in the lab but failure in real-world deployment.
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The Machine Learning Data TrapAjouté :
But, there is a massive trap hiding in this setup. Engineers build these multi-layered systems, hit 99% accuracy on their training data in the lab, and watch the entire system violently crash the moment it faces the real world. This failure is almost always caused by a single mechanical error known as data leakage. Data leakage happens when engineers train their level zero base models on a data set, and then ask those same models to generate predictions on that exact same data. Because the base models have already seen this data during training, they don't actually learn the underlying patterns. They just memorize the correct answers, producing artificially perfect scores.
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