Overfitting: a model that fits the training data too closely

Remember the correct answer, 'overfitting'.
・A state where the model fits the training data excessively and memorizes noise and fine details.
・Even with high accuracy on the training data, accuracy drops on unseen data and the model cannot generalize.
Underfitting is a different state where accuracy is low even on the training data, and regularization (a curbing technique) and hyperparameters (configuration values) are not names of 'states' in the first place.