Linear regression is a statistical model identifying a relationship between numeric variables. Given a set of objects described by the y attribute and the
x1, …, and
xn features, the model defines a relationship between the features and the attribute. The relationship is described by the linear function y = a0 + a1 * x1 + … + an * xn, and
a0, …, and
an are parameters defined by the method in such a way that the relationship is as close as possible to the data.
In the case of machine learning, linear regression can be used to predict a numeric attribute. The algorithm learns from the training dataset to determine the parameters. Then, given a new object, the model inserts its features into the linear function to estimate the attribute. ...