In the context of predictive modeling, what is "overfitting"?

Overfitting refers to a situation in predictive modeling where a model learns the training data too well, capturing noise and fluctuations instead of the underlying patterns. This results in a model that performs extremely well when evaluated on the training dataset, as it has essentially memorized the data. However, when this model is applied to unseen or new data, its performance often suffers because it fails to generalize beyond the specifics of the training set.

This phenomenon is particularly problematic because the ultimate goal of predictive modeling is to build models that can accurately predict outcomes for new data. Overfitted models lack this ability and tend to make errors on unfamiliar data, leading to unreliable predictions. Hence, the correct answer highlights the critical distinction between a model's performance on training data compared to its efficacy on unseen data, underscoring the importance of generalization in model building.

The other options touch on related concepts in predictive modeling but do not accurately represent the definition of overfitting. For example, a model that is "too simple" may struggle with underfitting rather than overfitting, and a model that "perfectly fits all data points" often indicates overfitting as well, but it does not explicitly capture the failure on unseen data. Lastly, a