What does overfitting mean in the context of machine learning?

Overfitting occurs when a machine learning model becomes excessively complex, to the point that it starts to learn not just the underlying patterns within the training data but also the noise and random fluctuations. This means that while the model may perform exceptionally well on the training dataset, yielding a low error rate and high accuracy, it struggles to generalize to new, unseen data.

In this scenario, the model has essentially memorized the training data instead of discovering the fundamental relationships, which results in poor performance on testing or validation datasets. This characteristic of overfitting highlights the importance of maintaining a balance between a model's complexity and its ability to generalize, hence avoiding purely fitting the peculiarities of the training data rather than the true signal it contains.

Therefore, recognizing and mitigating overfitting is a critical aspect of the machine learning pipeline.