Answer / Abhijit Kumar Sinha

Overfitting occurs when a model learns the training data too well, including its noise and outliers. This results in a model that performs well on the training set but poorly on new, unseen data (underfits or generalizes poorly). To avoid overfitting, techniques like cross-validation, regularization (L1 and L2), early stopping, and dropout can be used.

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