What does the term ‘overfitting’ refer to in machine learning?

Overfitting in machine learning refers to a scenario where a model learns not only the underlying patterns in the training data but also the noise and outliers present in that data. This occurs when a model is excessively complex, with too many parameters relative to the amount of training data, resulting in the model memorizing the training data instead of generalizing from it.

When a model memorizes the training data, it performs exceedingly well on that specific dataset but fails to generalize to new, unseen data. This means that while the training accuracy may be very high, the model's performance on validation or test datasets will typically reflect a significant drop in accuracy. Therefore, the successful identification of overfitting highlights the importance of employing techniques such as cross-validation, regularization, or using simpler models to ensure that the model generalizes well rather than merely memorizing the training inputs.

In contrast, failing to capture the trend of the data refers to underfitting, where the model is too simple. Similarly, a model performing well on both training and validation sets indicates that it is likely generalizing properly, and effectively generalizing to unseen data suggests a well-balanced model that has not overfitted.