What issue arises when a model learns noise from the training data?

When a model learns noise from the training data, it is referred to as overfitting. Overfitting occurs when a model becomes too complex, capturing not only the underlying patterns that are present in the training data but also the random fluctuations and anomalies, or "noise."

As a result, while the model may perform very well on the training data, it often struggles to generalize to new, unseen data. This diminishes its predictive performance, as the model is tailored too specifically to the idiosyncrasies of the training set instead of focusing on general trends.

In contrast, underfitting occurs when a model is too simple to capture the underlying patterns in the data, regression typically refers to a type of problem where the goal is to predict continuous values, and segmentation usually pertains to dividing data into distinct groups or categories. Each of these concepts is important in its own right, but none directly addresses the specific issue of a model picking up noise from the training data as overfitting does.