Linear regression belongs to the family of regression algorithms. The goal of regression is to find relationships and dependencies between variables. It is modeling the relationship between a continuous scalar dependent variable y (also, label or target in machine learning terminology) and one or more (a D-dimensional vector) explanatory variables (also, independent variables, input variables, features, observed data, observations, attributes, dimensions, data point, and so on) denoted x using a linear function. In regression analysis, the goal is to predict a continuous target variable, as shown in the following figure:
Figure 21: A regression algorithm is meant to produce continuous output. The input is allowed ...
