Linear VS Non-Linear Regression
Feature | Linear Regression | Non Linear Regression |
|---|---|---|
| Relationship between variables | Assumes a linear relationship between the independent and dependent variables | Allows for non-linear relationships between the independent and dependent variables |
| Model complexity | Simpler model with fewer parameters | More complex model with more parameters |
| Interpretability | Highly interpretable due to the linear relationship | Less interpretable due to the non-linear relationship |
| Overfitting susceptibility | More susceptible to overfitting due to its simplicity | Less susceptible to overfitting due to its ability to capture complex relationships |
| Flexibility | Requires large datasets to accurately estimate the linear relationship | Can work with smaller datasets due to its flexibility |
| Applications | Suitable for predicting continuous target variables when the relationship is linear | Suitable for predicting continuous target variables when the relationship is non-linear |
| Examples | Predicting house prices based on size and location | Predicting customer churn based on behavioral patterns |
Understanding Nonlinear Regression with Examples
In this article, we will see some examples of non-linear regression in machine learning that are generally used in regression analysis, the reason being that most of the real-world data follow highly complex and non-linear relationships between the dependent and independent variables.
Table of Content
- Non-linear regression in Machine Learning
- Assumptions in NonLinear Regression
- Types of Non-Linear Regression
- Non-Linear Regression Algorithms
- Evaluating Non-Linear Regression Models
- How does a Non-Linear Regression work?
- Linear VS Non-Linear Regression
- Applications of Non-Linear Regression
- Advantages & Disadvantages of Non-Linear Regression
- Frequently Asked Questions (FAQs) on Non-Linear Regression