what is label in machine learning

In machine learning, a label is a description that informs an ML model what a particular data represents so that it may learn from the example. It is the output variable that the model is trying to predict, and it represents the true outcome of the target. For example, in a supervised learning problem, the label is the correct answer that the model is trying to learn to predict. In data labeling, humans add one or more meaningful and informative labels to raw data to provide context so that a machine learning model can learn from it. The process of adding labels to data is known as data annotation or data labeling. Successful machine learning models are built on the shoulders of large volumes of high-quality training data, and the majority of models created today require a human to manually label data in a way that allows the model to learn how to make correct decisions.