RMSE Tutorial
RMSE Explained Simply
Root Mean Squared Error (RMSE) measures how far off your predictions are from the actual values — but instead of just averaging the differences, it squares them first, then averages those squares, then takes the square root to bring the number back to the original units.
Why it's useful: Like MAE, it's in the same units as your data, so an RMSE of 5 means your predictions are off by roughly 5 units on average. But because it squares the errors first, RMSE punishes big mistakes much more heavily than small ones — a single large miss can push RMSE up significantly, even if most of your other predictions were spot on.
Key contrast with MAE (good line for your video):
"RMSE treats every error the same way MAE does, direction doesn't matter, but size does. Squaring the errors means one big miss counts a lot more than several small ones. So if you care about being consistently close, look at MAE. If you care about avoiding occasional big blunders, RMSE is the better lens."
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