Evaluating Regression Models with R-Squared

When building machine learning models, knowing how well your model actually performs is just as important as training it. Understanding evaluation metrics like R-squared is essential for determining if your regression model is truly capturing underlying patterns or just memorizing noise. This text-only course guides you through the foundational concepts of regression analysis and the mathematical intuition behind the R-squared metric. You will learn to confidently interpret R-squared values, use adjusted R-squared to avoid common pitfalls, and apply these concepts to evaluate modern AI models. What you will learn: Understand the foundational math and logic behind the R-squared metric; Compare R-squared with other key regression metrics like MAE and RMSE; Identify model overfitting and underfitting using performance metrics; Apply adjusted R-squared to evaluate models with multiple features; Interpret metric outputs using clear Python code examples and explanations. The course starts with essential regression concepts and key terminology before walking you through step-by-step calculations and practical interpretation scenarios. You will then explore how to apply these metrics to real-world machine learning workflows to make informed model-selection decisions. This course is designed for beginner data scientists, product managers, and software developers who want to understand model evaluation without needing an advanced mathematical background. No prior experience with complex machine learning frameworks is required. Start reading today to master the core metrics that drive successful machine learning projects.