What is Polynomial Regression?
Polynomial regression is an extension of linear regression that models the relationship between the dependent variable and the independent variable(s) as an n-th degree polynomial. The equation for polynomial regression is:
[Tex]y = a0 + a1x + a2x2 + a3x3 +….+anxn[/Tex]
- where y is dependent variable
- x is the independent variable and
- a0,a1,a2,a3,an are the coefficients.
Polynomial Regression is useful for modeling non-linear relationships where the data points form a curve.
Linear vs. Polynomial Regression: Understanding the Differences
Regression analysis is a cornerstone technique in data science and machine learning, used to model the relationship between a dependent variable and one or more independent variables. Among the various types of regression, Linear Regression and Polynomial Regression are two fundamental approaches.
Linear Regression and Polynomial Regression
This article delves into the differences between these two methods, their applications, advantages, and limitations.
Table of Content
- What is Linear Regression?
- What is Polynomial Regression?
- Key Differences Between Linear and Polynomial Regression
- Understanding Practical Examples for Linear and Polynomial Regression
- When to Use Linear Regression vs. Polynomial Regression
- Implementing Linear Regression and Polynomial Regression
- Advantages and Disadvantages of Regression Models