Supervised Algorithms

Supervised algorithms are grouped under one category of machine learning called supervised learning. As the name implies, the whole process of learning is designed like a teacher monitors the learning process. The learning process starts with an input dataset, which has a set of features or attributes along with the outputs mapped one to one. Here the input is called the independent variables, and the output is called the dependent variables. The values of output are called labels, which enable the training process to correlate with the input features easier. In short, the mapping function is generated from the dataset, which has known inputs and outputs along with the training process. The predicted output is generated through a mapping function that calculates a label for each set of inputs for which the outputs are unknown. This chapter describes the supervised learning algorithms in detail along with their simple implementations in Python as case study.