root-mean-square deviation (RMSD)
What It Means
RMSD measures how far off your predictions or estimates typically are from the actual results. It's like calculating the average distance between where you aimed and where you actually hit the target, giving you a single number that represents your typical error.
Why Chief AI Officers Care
RMSD helps executives evaluate the reliability of forecasting models, risk assessments, and performance metrics - directly impacting decision quality and resource allocation accuracy.
Real-World Example
If your sales forecast model has an RMSD of $50,000, it means your monthly sales predictions are typically off by about $50,000 from actual results, helping you understand how much buffer to build into your planning.
Common Confusion
People often think RMSD shows the maximum possible error, but it actually shows the typical error - you could still have individual predictions that are much further off than the RMSD suggests.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI, RMSD is critical for validating predictive models like drug dosing algorithms, vital sign monitoring s...
Finance: In finance, RMSD is commonly used to evaluate the accuracy of risk models, trading algorithms, and forecasting systems b...
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- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
NISTNational Institute of Standards and Technology
"of an estimator of a parameter[; ...] the square-root of the mean squared error (MSE) of the estimator. In symbols, if X is an estimator of the parameter t, then RMSE(X) = ( E( (X−t)2 ) )½. The RMSE of an estimator is a measure of the expected error of the estimator. The units of RMSE are the same as the units of the estimator."Source: Glossary_of_Statistical_Terms
"a frequently used measure of the differences between values (sample or population values) predicted by a model or an estimator and the values observed"Source: Wikipedia_RMSD
Related Terms
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