What does overfitting in predictive modeling refer to?

Overfitting in predictive modeling refers to a situation where a model learns not only the underlying patterns in the training data but also the noise or random fluctuations. This results in a model that performs exceptionally well on the training data but fails to generalize to unseen data, leading to poor predictive performance on new observations.

When a model captures the noise along with the true signal, it becomes tailored to the specifics of the training dataset rather than the broader trends that apply across different datasets. This is why the correct choice emphasizes the model's tendency to capture noise instead of the meaningful signal, making it less reliable for making accurate predictions in real-world scenarios.

In contrast, other options focus on different issues related to model performance. For instance, not utilizing training data indicates a lack of learning, while a model that is too simple might not adequately capture the underlying complexities, leading to underfitting rather than overfitting. An overly general model would typically perform poorly on both training and testing data, whereas overfitting specifically denotes an excessive reliance on the training data, including its noise.