How to Solve Linear Regression Using Linear Algebra

Last Updated on August 9, 2019

Linear regression is a method for modeling the relationship between one or more independent variables and a dependent variable.

It is a staple of statistics and is often considered a good introductory machine learning method. It is also a method that can be reformulated using matrix notation and solved using matrix operations.

In this tutorial, you will discover the matrix formulation of linear regression and how to solve it using direct and matrix factorization methods.

After completing this tutorial, you will know:

• Linear regression and the matrix reformulation with the normal equations.
• How to solve linear regression using a QR matrix decomposition.
• How to solve linear regression using SVD and the pseudoinverse.

Kick-start your project with my new book Linear Algebra for Machine Learning, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

Tutorial Overview

This tutorial is divided into 6 parts;
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