There are 2 types of Linear regression based upon number of predictor variables. When we want to use this relationship to predict the value of one variable(dependent variable) based on the value of other variable(predictor variable), we use Linear Regression. If two numerical variables are linearly correlated, we will have their correlation coefficient value that falls between -1 and 1. The regression algorithm falls under Supervised Learning method where historic data is labelled and used to determine the value of the output variable. It is used to predict value of a variable (also called dependent variable), given the values of other variable/s (also called predictor variable/s).
Regression is defined as a statistical method that attempts to determine a relationship between two or more correlated variables.
0 kommentar(er)
