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.
I will give an example where they are not interchangeable.
Label and target both can express the meaning of y depending on x, but only label has a meaning of describing the input, for example:
In image classification: a training example (cat image pixels, cat), we can say the cat is the label of this image because it's just describe the kind of this image.
But in word2vec in which we use current word to predict its context word: a training example, say (orange, juice), here we may not say juice is the label of orange, very strange right? so in this scene, we can only say juice is the target of the orange, only express it's the dependency.
