What term describes the overall measure of how well a model performs on data outside its training set?

The term that best describes the overall measure of how well a model performs on data outside its training set is generalization performance. Generalization performance refers to the model's ability to apply what it learned from the training data to unseen data, which is crucial for assessing its effectiveness in real-world scenarios. A model that exhibits strong generalization performance can make accurate predictions or classifications on new inputs it has not encountered during training, reflecting its understanding of the underlying data patterns rather than simply memorizing the training set.

Validation accuracy, while important, specifically pertains to the accuracy obtained on a validation set, which is part of the training process but does not necessarily indicate how well the model will perform on entirely new data. Model robustness focuses on the model’s performance stability in the face of varying conditions or slight alterations in input data, rather than its ability to generalize. Predictive power relates to the model’s potential effectiveness in making accurate predictions, but it does not encapsulate the concept of performance on unseen data as directly as generalization performance does. Thus, the correct term is generalization performance.