
Introduction
Last Updated on July 24, 2023 by Editorial Team
Author(s): ___
Originally published on Towards AI.
In this article, I will explain why when building a linear model, we add an x₁x₂ term if we think the variables x₁ and x₂ interact and conclude with a principled method to add interaction terms.
The content of this article is based on Chapter 8 of [1].
I assume the reader has a basic understanding of how linear models work.
Let’s begin by building a model with no interaction terms.
Suppose we would like to model y as a function of x₁ and x₂. Then, a linear model that describes this relationship is:
Figure 1: A linear model with no interaction terms
We call α… Read the full blog for free on Medium.
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI
Take our 90+ lesson From Beginner to Advanced LLM Developer Certification: From choosing a project to deploying a working product this is the most comprehensive and practical LLM course out there!
Towards AI has published Building LLMs for Production—our 470+ page guide to mastering LLMs with practical projects and expert insights!

Discover Your Dream AI Career at Towards AI Jobs
Towards AI has built a jobs board tailored specifically to Machine Learning and Data Science Jobs and Skills. Our software searches for live AI jobs each hour, labels and categorises them and makes them easily searchable. Explore over 40,000 live jobs today with Towards AI Jobs!
Note: Content contains the views of the contributing authors and not Towards AI.