Definition of "Ground Truth"

[Nimmi Srivastav]

Hello,

I am having the toughest time finding a good definition of "Ground Truth". Here’s what I have found so far:

(1) In supervised learning, ground truth means that you have data instances that are labeled in accordance with your goal, such as “responsive” and “non-responsive” or “privileged” and “non-privileged.” If this information is available for a small subset of one’s entire collection, it can be used to build (infer) a model. With unsupervised learning, on the other hand, no such labels are available.

(2) "Ground truth" means a set of measurements that is known to be much more accurate than measurements from the system you are testing.

(3) Sometimes synthetic data are generated from a model, to test a system whose goal is to estimate parameters of the model. In such cases the "ground truth" is the known parameters of the model.



Are these definitions correct?

Thanks,
NS