Answer / Lilpat Singh

Overfitting occurs when a model learns the training data too well, capturing noise instead of general patterns. This results in poor performance on unseen data. Underfitting occurs when a model is too simple to capture the underlying pattern in the data, also resulting in poor performance on both the training and test data.

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