Answer Posted / Nikhiul Saxena

Overfitting occurs when a model learns the training data too well, resulting in poor generalization performance on unseen data. This can be combated by techniques such as regularization, dropout, and early stopping. Underfitting, on the other hand, refers to a situation where the model is unable to learn the underlying patterns in the data. To address underfitting, one might consider increasing the complexity of the model or gathering more training data.

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