What does the term 'overfitting' refer to in data mining?

The term 'overfitting' refers specifically to a situation in data mining and machine learning where a model learns the training data too well, capturing noise and outliers in addition to the underlying patterns. This happens when the model is overly complex, allowing it to fit the training data very closely. As a result, while the model may perform exceptionally well on this training data (sometimes achieving very high accuracy), it fails to generalize to new, unseen data. Thus, it can produce poor results when making predictions outside the training dataset.

By understanding overfitting, practitioners in data mining can recognize the importance of using techniques such as cross-validation, choosing simpler models, or applying regularization methods to maintain a balance between bias and variance, ensuring the model not only fits the training data but also performs well on new data.