The user wants to understand Overfitting in the context of Machine Learning Foundations and apply it to practical AI governance or compliance work.
Overfitting occurs when a model learns the training data too closely, including noise or idiosyncratic details, and performs poorly on new data. It is commonly indicated by high training performance but weaker validation or test performance.
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