Evaluating Non-Linear Regression Models
Evaluating the performance of a nonlinear regression model is crucial to ensure it accurately represents the underlying relationship between the independent and dependent variables.
There are a number of different metrics that can be used to evaluate non-linear regression models, but some common metrics are:
- R-squared – R-squared (Coefficient of Determination) measures the proportion of variance in the dependent variable that is explained by the independent variables in the model. It ranges from 0 to 1, where 0 indicates no explanation of variance and 1 indicates perfect explanation. A higher R-squared value suggests a better model fit.
- Adjusted R-squared – Adjusted R-squared is a modified version of R-squared that accounts for the number of independent variables in the model. It penalizes models with more variables, making it a more appropriate measure of goodness of fit when comparing models with different numbers of independent variables. A higher adjusted R-squared value indicates a better model fit.
- Root Mean Squared Error (RMSE) – Root Mean Squared Error (RMSE) is the square root of MSE, providing a more intuitive measure of the average error in predictions. It represents the average distance between the predicted and actual values of the dependent variable, scaled to the same units as the dependent variable. A lower RMSE signifies a better model fit.
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