What is Polynomial Regression?
Polynomial regression is a technique used to model the relationship between a dependent variable (what you’re trying to predict) and an independent variable (what you’re basing your prediction on) when that relationship isn’t straight line. Polynomial regressions are capable to fit curves by leveraging polynomial equations. Hence, the complexity of the curve is dependent on the degree of polynomial.
Advantages of Polynomial Regression
- Ability to capture relationships between variables by fitting higher-degree polynomial functions
- They do not assume linear relationship between the independent and dependent variables.
Limitations of Polynomial Regression
- There is a risk of overfitting, when the model captures noise in the data leading to poor performance on test set.
- Can produce unbounded predictions, particularly when extrapolating beyond the range of the observed data.
Polynomial Regression vs Neural Network
In this article, we are going to compare polynomial regression and neural networks.