What does the term "overfitting" refer to in machine learning?

The term "overfitting" in machine learning specifically refers to the situation where a model learns not just the underlying patterns in the training data, but also the noise—random fluctuations and outlier points that do not generalize well to new, unseen data. When a model is overly complex, it may fit the training data very closely, which can lead to high accuracy in the training set but poor performance on validation or test sets. This happens because the model has become too tailored to the specifics of the training data, failing to generalize to broader patterns that apply to other datasets.

In contrast to overfitting, a model that is too simple would not capture the complexities within the data, leading to underfitting. Failing to appreciate the underlying relationships in the data is also a characteristic of underfitting, where pertinent features and influences are ignored. Improperly scaling data features tends to affect how well a model learns, but it is not a direct result of the overfitting process itself. Thus, while all options reference potential issues in model training and predictions, overfitting specifically emphasizes the balance between bias and variance, with a focus on capturing excessive noise inherent in the training data.