In machine learning, what does the term "overfitting" refer to?

The term "overfitting" in machine learning refers to a model that has learned the training data too well, including the noise and outliers present within that data. This phenomenon occurs when a model is excessively complex, capturing not only the underlying patterns but also the random fluctuations and anomalies that do not generalize to unseen data. As a result, while the model may perform exceptionally well on the training dataset, its ability to predict or make accurate decisions on new, unseen data is significantly impaired. Essentially, overfitting leads to poor generalization, as the model becomes too tailored to the specific nuances of the training set rather than learning broader patterns that apply to a wider range of scenarios.

In contrast, the other options describe scenarios that do not accurately represent overfitting. Generalizing well to new data indicates a model that is balanced and learns effectively, while a model that avoids learning from the training data or is incapable of learning suggests underfitting, which is the opposite issue of overfitting.