# Polynomial Regression

## A Simple Explanation - By Varsha Saini

In the Regression problem, it is assumed that the relationship between independent variables and the dependent variable is linear. But it may be possible that the relationship is non-linear.

When the relationship is non-linear, creating a straight line may not be the best decision boundary, we need to have a decision boundary that can fit the data well. Hence higher polynomial degree features are included in the model to make a non-linear decision boundary.

Polynomial Regression is also used to solve the problem of Underfitting.

Polynomial regression is a special kind of Linear Regression that includes the feature of n polynomial degree.

where

• y = predicted output
• b0 = intercept, bias
• x1 = independent variable
• = second polynomial degree of feature 1
• = third polynomial degree of feature 1
• = n polynomial degree of feature 1
• b1,b2,..,bn = coefficient of corresponding features
• n polynomial degree is the highest degree feature present.