Unveiling the Curve: Exploring Polynomial Regression
“Polynomial regression: Because not all relationships are straight lines.” — Unknown
Introduction
Regression is a set of methods used in statistics and machine learning to identify relationships between variables and forecast values.
Different regression models serve distinct purposes. Understanding the underlying differences can help a data practitioner select the correct model.
In a previous article, I explained linear regression, an effective technique for variables with a linear relationship. This article focuses on polynomial regression, a powerful method for addressing nonlinear data.
I’ll start by addressing the definition of polynomial regression in the context of machine learning and its usefulness for nonlinear relationships. Following, I’ll apply polynomial regression on real-world data to explain the mathematical inner workings of polynomial regression. The goal of the model used is to predict the number of deaths in the Netherlands using temperature data.
What is Polynomial Regression?
Polynomial regression is a statistical technique and a supervised machine learning model that can forecast continuous quantities. It is effective for variables that show a…
