Supervised Learning—Linear Regression
Wei-Meng Lee · 2019
This chapter looks into linear regression in more detail and discusses another variant of linear regression known as polynomial regression. It also discusses the following: multiple regression, polynomial regression, and polynomial multiple regression. The chapter helps the coders to use multiple linear regressions to train a model based on the Boston dataset. The chapter also helps readers to learn how to plot the hyperplane by showing the relationships between two independent variables and the label. In machine learning, linear regression is one of the simplest algorithms that the coders can apply to a dataset to model the relationships between features and labels. The inability for a machine learning algorithm to capture the true relationship between the variables and the outcome is known as the bias. A positive correlation is a relationship between two variables in which both variables move in tandem.