Azure Data Scientists Associate Practice Exam

Overfitting in the context of machine learning refers to a situation where a model learns not just the underlying patterns in the training data but also the noise and fluctuations that may be present. This leads to a model that is overly complex, capturing details that do not generalize to unseen data. As a result, while the model performs exceptionally well on the training set, its performance on new, unseen data tends to be poor because it is unable to properly distinguish between relevant patterns and random noise.

This phenomenon typically occurs when the model is too flexible or has too many parameters relative to the amount of training data available. Thus, it essentially memorizes the training data rather than learning to generalize from it. In practical terms, overfitting can be mitigated through techniques such as cross-validation, regularization, and pruning, which help improve the model's ability to generalize to new data.