What does "overfitting" refer to in machine learning?

Overfitting refers specifically to a model learning the training data too well, capturing noise and outliers rather than the underlying patterns. This excessive complexity leads to the model performing exceptionally well on the training dataset but poorly on unseen data. In other words, an overfitted model can exhibit very low error rates during training, but when evaluated on new, unseen data, it fails to generalize, resulting in high error rates. Thus, the concept of overfitting highlights the importance of a model's ability to generalize beyond the specific examples it was trained on, making the performance on unseen data a critical measure of a model's effectiveness.

While the other options touch on aspects of model performance and data learning, they do not specifically define overfitting in the context of machine learning as clearly as the idea of poor performance on unseen data does.