What is meant by the term 'overfitting' in a model?

The term 'overfitting' refers to a situation in model training where the model learns not just the underlying patterns in the training data, but also the noise and random fluctuations present in that data. This results in a model that performs extremely well on the training dataset, as it has essentially memorized it, but performs poorly on unseen data. The model becomes overly complex and specific to the training dataset, which reduces its generalization capability to new, unseen data.

This concept is crucial in data mining and machine learning, as the goal is to build models that generalize well to other data rather than ones that simply reflect the quirks of the training set. Understanding overfitting helps practitioners to create more robust models by implementing techniques like cross-validation, regularization, and pruning to prevent a model from being too complex.