ABSTRACT
In this chapter, the authors want to delve deeper into understanding of a technique in machine learning, which is supervised learning. The list of the real-work practical applications of the regression analysis of the supervised learning to solve several linear and nonlinear problems is inexhaustive, while the literature is replete with several studies that have deployed these algorithms across several fields for intelligent decision-making, planning, and designs. Support vector machines, decision trees, and logistic regression are popular binary classification techniques. Multi-class classification uses logistic regression, decision trees, Support Vector Machine, and neural networks. Regression analysis gives insights into the understanding of the trend in the variation of the independent variable when one or more independent variables change. The authors begin by creating a decision tree at the root node, which stands in for the entire dataset.
