The Best Practices Of Linear Regression For Machine Learning

A linear regression model assumes that the regression function E (Y |X) is linear in the inputs X۱,.…, XP . Linear models were largely developed in the precomputer age of statistics, but even in today’s computer era there are still good reasons to study and use them. linear regression is still a useful and widely applied statistical learning method. Moreover, it serves as a good starting point for more advanced approaches; They are simple and often provide an adequate and interpretable description of how the inputs affect the output. For prediction purposes they can sometimes outperform fancier nonlinear models, especially in situations with small numbers of training cases, low signal-to-noise ratio or sparse data. Finally, linear methods can be applied to transformations of the inputs and this considerably expands their scope. These generalizations are sometimes called basis-function methods. In this paper some important linear methods will be discussed.

Keywords:

linear regression , Linear models , prediction purposes , nonlinear models , basis-function methods