difficulty 1/5
RMSE
The square root of the MSE — the same error measure, brought back into the units of the thing being predicted so a human can read it.
Intuition
MSE has one practical flaw: its units are the square of the target's. Predict house prices in dollars and MSE comes back in dollars squared, which nobody can interpret and no stakeholder will accept.
RMSE takes the square root and puts the number back in dollars. Nothing else changes — the ranking of models by RMSE is identical to their ranking by MSE, because the square root is monotonic. It is a presentation fix, and a valuable one.
Formal definition
How it works
Where it's used
Why & when
Worked example
Pitfalls
Check yourself
3 questions
The questions on this page come with access. They are graded on the server, so the answers stay there until you have given yours.
On the cheat sheet: Regression metrics and losses →
Sources
- scikit-learn — sklearn.metrics.root_mean_squared_errordocs · retrieved 2026-08-30https://scikit-learn.org/stable/modules/generated/sklearn.metrics.root_mean_squared_error.html
- scikit-learn User Guide — Metrics and scoringdocs · retrieved 2026-10-04https://scikit-learn.org/stable/modules/model_evaluation.html
- Dive into Deep Learning — 3.1 Linear Regressionbookhttps://d2l.ai/chapter_linear-regression/linear-regression.html
- Think Stats — Ch. 10, Linear least squares, 2nd ed.book · retrieved 2026-10-06https://greenteapress.com/thinkstats2/html/thinkstats2011.html