When To Use Polynomial Regression Model In Place Of Linear Regression Model

Polynomial Regression Model Vs Linear Regression Model

Linear regression model is model where the continious response variable is predicted based upon one or more predictor variables.It is assumed that the relationship between the  predictor variable and response variable is linear.Linea model genarlly describes a continious variable as the function for one or more predictor variables.These variables can help you to understand and predict a complex system or can help to analyze experimental.financial and biological data.

Whereas the polynomial regression can be useful when you find the relationship between the predictor variable and the response variable as non linaer.Non Linear model generally depicts the non linaer equation between the predictor variable and the response variable.Non Linear models are genrally assumed to be parametic model where the model is calculated with anon linear equation.Generally the machine learning methods are non-parametric nonlinear regression.

You can use the polynomail regression mopdel when the trelation between the Predictor and Response variable is not represented by staright line.Multiple predictor variables and it's complexities can cause a linaer model to fail top make the correct prediction of a model.So in such scenario you can plan to develop a Polynomial Regression model to get the correct prediction by the Model.