We currently have a tag testing with the description (written by myself)
For questions related to the concept of testing or evaluating machine learning models and algorithms.
In supervised learning, testing usually refers to computing some performance measure (e.g. accuracy) on some dataset that was not used for training or validation (i.e. early stopping, k-fold cross-validation, or hyper-parameter optimization). It seems to me that "evaluation" doesn't necessarily refer to this specific meaning of testing, but it's more general (or maybe I should say vague or not well-defined), and it may refer also to any type of measurement that quantifies some property of the model or algorithm. So, given this view, validation could also be some form of "evaluation" (for example, it would be a way to measure whether the model is over-fitting or not).
Let's take, for example, this question: How to evaluate an RL algorithm when used in a game?. The user is asking "how to evaluate this RL algorithm". I've tagged the question with testing, but, at this point, I'm not sure whether this is a good idea or not. So, should "evaluation" be a synonym for "testing"? If not, what should we use it for?