What does the term 'overfitting' refer to in the context of decision trees?

The term 'overfitting' specifically describes a situation in which a decision tree model captures not just the underlying patterns in the training data but also the noise or random fluctuations present in that data. This leads to a model that performs exceptionally well on the training dataset but fails to generalize to unseen data or test sets.

By memorizing these noise details, the model becomes highly complex and may create overly intricate rules that do not reflect the true relationships within the data. As a result, although the accuracy appears high during training, its performance drops significantly when applied to new data.

This phenomenon illustrates the critical balance needed in machine learning between fitting a model accurately to the training data while ensuring that it can also perform well on new, unseen data, which is a fundamental goal of predictive modeling.