Read about Features(only 1 min. read) to fully understand label.
What is Label?
Output or the target variable that the model is trying to predict.
For Example,
Continuing with the house example, if the goal is to predict the price of houses, then the "price" would be the label. The model learns to associate patterns in the features with corresponding values in the labels.
Continuing with Person example, if the goal is to predict salary of person based on provided features in the dataset, the label could be "salary"
Continuing with music playlist example, label could be "Hit or Flop".
Basically, we are providing examples to model.
So, when we ask AI/ML model to predict whether a person is earning more than 50K per month or not, it will check person's age and marital status, and respond accordingly. However, for more accurate results, we requires more features, to correctly label data.
