Root Mean Square Error (RMSE)
is a regression metric that measures the average magnitude of prediction errors by calculating the square root of the average squared differences between predicted and actual values.
What is Root Mean Square Error (RMSE)?
RMSE compares a model's predicted values with the actual values in a dataset. Because errors are squared before being averaged, larger errors have a greater effect on the final RMSE value. RMSE is expressed in the same units as the target variable.
Why is Root Mean Square Error (RMSE) Important?
RMSE provides a clear measure of how far predictions typically deviate from actual values while giving greater weight to larger errors. A lower RMSE generally indicates that predictions are closer to the observed values.
Common use cases
RMSE is commonly used to evaluate regression models, forecasting systems, demand prediction, financial modeling, and other applications involving continuous numerical predictions.