In neural network training, what does the term "overfitting" refer to?

The term "overfitting" refers to a scenario in neural network training where a model captures noise and fluctuations in the training data instead of the intended underlying patterns. This results in the model performing exceptionally well on the training data, where it has essentially memorized the examples it was trained on, but poorly on unseen or validation data. The key aspect of overfitting is that the model fails to generalize; it does not adapt well to new data, which is a critical requirement for successful machine learning applications.

In contrast, the other options focus on different aspects of model performance. A model performing poorly on training data would suggest issues such as underfitting, where the model fails to capture the underlying structure adequately. A model that generalizes well to new data would be the opposite of overfitting, as it would indicate a healthy model that balances fitting training data while maintaining performance on unseen examples. Lastly, a model with too few parameters typically suffers from underfitting rather than overfitting, as it may lack the capacity to learn the training data's complexity adequately. Thus, the correct interpretation of overfitting is captured precisely by the chosen option.