Difference between a target and a label in machine learning
Target: final output you are trying to predict, also know as y. It can be categorical (sick vs non-sick) or continuous (price of a house).

Label: true outcome of the target. In supervised learning the target labels are known for the trainining dataset but not for the test.

Label is more common within classification problems than within regression ones. Nonetheless, they are often used interchangeably without great precision.