In machine learning, what does the term 'overfitting' typically refer to?

The term 'overfitting' in machine learning specifically refers to a situation where a model learns the details and noise in the training data to the extent that it negatively impacts its performance on new, unseen data. When a model is overfit, it captures the random fluctuations in the training data rather than the underlying pattern. This results in excellent performance on the training dataset, where it can accurately predict outcomes, but it struggles to generalize to new data, leading to poor performance when tested on datasets it has not encountered before.

Understanding overfitting is crucial because it highlights the importance of using techniques like cross-validation, regularization, and pruning to ensure models generalize well to new data, rather than just memorizing the training set. Other concepts like model complexity and feature selection may relate to how overfitting occurs but do not encapsulate its definition as accurately as the notion of poor performance on unseen data.