Demystifying the R² Score: The Ultimate Guide to Understanding, Calculating, and Mastering It

Introduction

Imagine you’ve built a machine learning model to predict house prices. You train it, test it, and… voilà! But how do you know if your model is any good? Enter the R² score , also known as the coefficient of determination.

The R² score is one of the most widely used metrics for evaluating regression models. It tells you how well your model explains the variability in your data compared to a baseline (usually the mean). Whether you’re a beginner or an experienced data scientist, understanding the R² score — and its underlying math — will help you make smarter decisions when building predictive models.

In this article, we’ll break down everything about the R² score:

• What it means and why it matters.
• The mathematical formula behind it (don’t worry — it’s simpler than it looks!).
• How to interpret its values and avoid common pitfalls.
• Real-world examples to solidify your understanding.

Let’s dive in!

What Is the R² Score?

A Measure of Goodness-of-Fit

The R² score quantifies how well your regression model fits the observed data. Specifically, it measures the proportion of variance in the dependent variable (the target) that is predictable from the…