What does the term 'overfitting' refer to in machine learning?

The term 'overfitting' in machine learning refers to a situation where a model learns the details and noise in the training data to such an extent that it negatively affects the model's performance on new data. This means that while the model may perform exceptionally well on the training dataset, achieving high accuracy, it fails to generalize to unseen data, leading to poor performance on test datasets.

Overfitting typically occurs when a model is too complex, allowing it to capture not just the underlying patterns but also the random fluctuations in the training data, which do not reflect the broader trends in the population. As a result, the model becomes sensitive to the specificities of the training data, making it less effective when applied to other datasets.

In contrast, the other options present scenarios that do not accurately describe overfitting. For instance, maintaining high accuracy on all datasets suggests generalization, which is the opposite of overfitting. Similarly, performing poorly on both training and test data indicates a different problem known as underfitting, while demonstrating perfect performance during training aligns more with overfitting but does not capture the essence of the model's failure to generalize. Thus, the correct understanding of overfitting is that it reflects an excessive adaptation