Supervised Learning is a machine learning paradigm for acquiring the input-output relationship information
of a system based on a given set of paired input-output training samples. As the output is regarded as the
label of the input data or the supervision, an input-output training sample is also called labelled training data,
or supervised data. Occasionally, it is also referred to as Learning with a Teacher (Haykin 1998), Learning
from Labelled Data, or Inductive Machine Learning (Kotsiantis, 2007). The goal of supervised learning is to
build an artificial system that can learn the mapping between the input and the output, and can predict the
output of the system given new inputs. If the output takes a finite set of discrete values that indicate the class
labels of the input, the learned mapping leads to the classification of the input data. If the output takes continuous
values, it leads to a regression of the input. The input-output relationship information is frequently
represented with learning-model parameters. When these parameters are not directly available from training
samples, a learning system needs to go through an estimation process to obtain these parameters. Different
form Unsupervised Learning, the training data for Supervised Learning need supervised or labelled information,
while the training data for unsupervised learning are unsupervised as they are not labelled (i.e., merely
the inputs). If an algorithm uses both supervised and unsupervised training data, it is called a Semi-supervised
Learning algorithm. If an algorithm actively queries a user/teacher for labels in the training process, the iterative
supervised learning is called Active Learning.