What are the characteristics of overfitting and underfitting in neural networks?

$$ Overfitting occurs when the model performs well on both training and unseen data, while underfitting occurs when the model performs poorly on the training

$*$ data.

Overfitting and underfitting have the same characteristics and are indistinguishable in neural networks.

Overfitting occurs when the model performs poorly on both training and unseen data, while underfitting occurs when the model performs well on the training data but poorly on unseen data.

$$ Overfitting occurs when the model performs well on the training data but poorly on unseen data, while underfitting occurs when the model performs poorly on both training and$$unseen$$data$.$