Abstract
Numerous domains now employ learning algorithms. It has distinct
performance metrics appropriate for them.. Based on a predetermined set of paired
input-output training samples, a machine learning paradigm known as “Supervised
Learning” is used to gather information about a system's input-output relationship. An
input-output training sample is also known as supervised or labeled training data
because the output is regarded as the input data or supervision label. Supervised
learning aims to build an artificial system that can learn the mapping between input and
output and predict the system's output, given new information. The learned mapping
results in the classification of the input data if the output takes a limited set of discrete
values representing the input's class labels. Regression of the information occurs if the
output takes continuous values. The chapter details the various algorithms,
technologies used and their applications.