The correct answer is False.
A true positive is a correct positive identification. In other words, it is a case where the model correctly identifies a positive instance. For example, if the model is trying to identify spam emails, a true positive would be an email that the model correctly identifies as spam.
A false positive is an incorrect positive identification. In other words, it is a case where the model incorrectly identifies a negative instance as positive. For example, if the model is trying to identify spam emails, a false positive would be an email that the model incorrectly identifies as spam, when it is actually a legitimate email.
Therefore, a true positive does not mean correctly rejected. A true positive means correctly identified.