Difference between a target and a label in machine learning If I have a supervised learning system (for example for the MNIST dataset) I have features (pixel values of MNIST data) and labels (correct digit-value).

However sometimes people use the word target (instead of label).

Are target and label interchangeable? Is label just used for classification? Target both for classification and regression?
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.