Answer Posted / Neelambuj Kumar Shrivastava

Overfitting occurs when a machine learning model learns the training data too well, to the point where it performs poorly on new, unseen data. The model may capture noise or irrelevant patterns in the training data that are not representative of the actual problem. Underfitting, on the other hand, happens when the model is too simple and cannot capture the underlying relationships in the data, resulting in poor performance both during training and testing.

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