Episode 11 — Compare regression performance measures: RMSE, MAE, MAPE, and R-squared
This episode teaches how to compare common regression metrics in a way that matches how the DY0-001 exam expects you to reason about error, fit, and practical impact. You’ll define RMSE, MAE, MAPE, and R-squared, then connect each one to what it rewards and what it hides, such as RMSE’s sensitivity to large errors, MAE’s more even penalty, MAPE’s pitfalls with small or zero values, and R-squared’s tendency to look better simply by adding features. We’ll walk through exam-style scenarios where two models trade places depending on the metric, and you’ll learn how to justify a choice based on business context, units, and tolerance for outliers. You’ll also cover troubleshooting moves like checking residual patterns, validating on held-out data, and avoiding metric misuse when the target distribution is skewed or seasonal. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with.
