Comparative Study of Supervised Regression Algorithms in Machine Learning

Recently regression has gained a lot of interest in the research in both statistics and machine learning (ML) and it is applicable in a wide range of real life problems. This approach is a supervised learning technique applied for predicting continuous data and searching for a model to distinguish the real values data. This means to determine the mapping function between one dependent variable and many independent values. There are several algorithms that are used in regression. This research focuses on comparative study of the different forms of regression techniques, namely the most frequently used ones including Linear Regression (LR), Support Vector Regression (SVR), and Regression Tree (RT) with their strengths and limitations.