Key Differences Between Linear and Polynomial Regression
Model | Nature of Relationship | Model Complexity | Flexibility |
---|---|---|---|
Linear Regression | Assumes a straight-line relationship between dependent and independent variables. Suitable for linear trends. | Simpler, easier to interpret, fewer parameters, less prone to overfitting. | Limited to linear relationships, may underfit non-linear data. |
Polynomial Regression | Can model non-linear relationships by fitting a polynomial equation to the data. Ideal for complex patterns. | More complex, higher-degree polynomial, more prone to overfitting. | Offers greater flexibility, can model curves and intricate patterns, suitable for non-linear trends. |
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.
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