- See also: Machine learning terms
In machine learning, a label refers to the desired output, or the "correct" value, for a particular instance in a dataset. Labels are used in supervised learning algorithms, where the goal is to learn a mapping from input data to output data, based on a set of examples containing input-output pairs. These output values in the training dataset are known as labels. The process of assigning labels to instances in the training dataset is called labeling or annotation.
Labels can be of different types, depending on the nature of the problem being solved by the machine learning algorithm. The two main types are:
Categorical labels are used when the machine learning task is to classify instances into one of several distinct categories. The categories can be unordered, as in classification problems, or ordered, as in ordinal regression problems. In classification problems, the labels are often called classes.
Numerical labels are used when the machine learning task is to predict a continuous value, as in regression problems. These labels are real numbers, and the goal of the algorithm is to minimize the error between the predicted value and the actual label.
The quality of the labels in a training dataset plays a crucial role in the success of a machine learning algorithm. Poorly labeled data can lead to incorrect or biased models, which, in turn, can result in reduced performance or even harmful outcomes when deployed. Label quality can be impacted by several factors:
Noise refers to random errors or inconsistencies in the labels, which can be introduced during the labeling process. Noise can arise due to human error, measurement errors, or other sources of variability.
Ambiguity occurs when it is unclear which label should be assigned to an instance, often due to inherent uncertainty in the data or the task itself. Ambiguity can result in inconsistent labeling and can make it challenging for the algorithm to learn the correct mapping from inputs to outputs.
Bias refers to systematic errors in the labels, which can lead to biased models. Bias can be introduced during the labeling process due to human prejudice, sampling errors, or other factors that systematically favor one label over another.
Explain Like I'm 5 (ELI5)
In machine learning, a label is like the answer to a question. When we teach a computer how to do something, like tell the difference between cats and dogs, we give it lots of examples of cats and dogs with the correct answer (label) attached to each example. The computer uses these examples to learn the patterns that help it tell cats and dogs apart. So, labels are the correct answers we provide to help the computer learn from examples.