Overfitting happens when an AI model learns its training data too closely, including its noise and quirks, so it performs well on those examples but poorly on new data. In effect, the model memorizes instead of generalizing. It is a common pitfall, and techniques such as using more data, simplifying the model, or holding back a test set help detect and prevent it.
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