What term describes the error a model makes when predicting on new, unseen data?

The correct term that describes the error a model makes when predicting on new, unseen data is generalization error. Generalization error specifically refers to how well the model performs on data that it has not encountered during training. It is an important measure because the ultimate goal of any predictive model is not just to perform well on the training data but to generalize well to new, unseen data.

Generalization error encompasses all factors that contribute to a model's performance in real-world scenarios where the input data can differ significantly from the training data. This concept is vital in understanding model robustness and is closely related to the model’s ability to learn patterns rather than just memorizing the training examples.

In contrast, training error measures how accurately the model predicts data it has already seen during the training process, which does not provide insight into its ability to perform on new data. Validation error measures the performance on a validation dataset that was set aside during the model training process, but still does not fully capture generalization to external data. Residual error, on the other hand, refers to the difference between the actual values and the predicted values for individual data points, which does not specifically address the performance on new, unseen instances.

Overall, generalization error is crucial for evaluating