Answer / Amrish Soam

Overfitting occurs when a machine learning model learns the training data too well, capturing noise and idiosyncrasies in addition to the underlying pattern. As a result, the model performs well on the training data but poorly on new, unseen examples (test data). To avoid overfitting, techniques such as regularization, cross-validation, early stopping, and using more data can be employed.

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