generalizationconsistency
Generalizationconsistency is a term used to describe a property of learning systems in which the generalization performance remains stable and converges toward a predictable target as data or tasks increase in quantity or evolve in distribution. In practice, it often refers to the idea that a model’s performance on unseen data becomes reliably close to its performance on the training data or to the best possible performance under a given distribution, as the amount of data grows.
In formal terms, generalization consistency can be linked to standard concepts from statistics and learning theory.
Praxis considerations include controlling model complexity, regularization, and techniques such as cross-validation to ensure that generalization