Answer:
The supervised classification algorithms usually operate on the information provided by a set of samples, patterns, examples or training prototypes that are assumed as representatives of the classes, and have a correct class label. This set of correctly labeled prototypes is known as a training set, and is the knowledge used to classify new samples.
Step-by-step explanation:
The objective of this type of algorithm is to determine what is the class, of which there is already knowledge, to which a new sample must belong, based on the information that can be extracted from the training set. Among the supervised classification algorithms are those that use neighborhood criteria. As an example, there are supervised classifiers based on neighborhood criteria, where the closest neighbor rule (NN rule) and the closest neighbor k rule (k-NN rule).
