What is meant by "ground truth" in the context AI? What does &quot;ground truth&quot; mean in the context of AI especially in the context of machine learning?
I am a little confused because I have read that the ground truth is the same as a label in supervised learning. And I think that's not quite right. I thought that ground truth refers to a model (or maybe the nature) of a problem. I always considered it as something philosophical (and that's what also the vocabulary 'ground truth' implies), because in ML we often don't build a describing model of the problem (like in classical mechanics) but rather some sort of a simulator that behaves like it is a describing model. That's what we/I call sometimes black box.
What is the correct understanding?
In the context of ML, ground truth refers to information provided by direct observation (empirical evidence). If you're training an algorithm to classify your data, then the ground truth will be the actual, true labels which could for example be manually annotated by an domain expert. Please note that the models prediction or the inferred labels, are not considered ground truth.