In the context of Machine Learning, I have seen the term Ground Truth used a lot. I have searched a lot and found the following definition in Wikipedia:
In machine learning, the term "ground truth" refers to the accuracy of the training set's classification for supervised learning techniques. This is used in statistical models to prove or disprove research hypotheses. The term "ground truthing" refers to the process of gathering the proper objective (provable) data for this test. Compare with gold standard.
Bayesian spam filtering is a common example of supervised learning. In this system, the algorithm is manually taught the differences between spam and non-spam. This depends on the ground truth of the messages used to train the algorithm – inaccuracies in the ground truth will correlate to inaccuracies in the resulting spam/non-spam verdicts.
The point is that I really can not get what it means. Is that the label used for each data object or the target function which gives a label to each data object, or maybe something else?