Name
Abstract | ||
|---|---|---|
Metamodels are used to provide more efficient predictions than the underlying simulation models do, but at the price of reduced prediction accuracy. Statistics used to quantify this prediction accuracy include the root-mean square error (RMSE), the coefficient of determination R-square, and the average absolute error (AAE). Such statistics depend on the average prediction accuracy over the validation sample; i.e., these metrics are sensitive to the size of the validation sample. This article, therefore, introduces a new metric, called the Model acceptability score (MAS). Preliminary results indicate that MAS is less sensitive to the validation sample size. The article focuses on deterministic simulation, which is used in various engineering disciplines, e.g., electronic engineering. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/s00366-010-0200-z | Eng. Comput. (Lond.) |
Keywords | Field | DocType |
root-mean square error,deterministic simulation,validation sample size,prediction accuracy,efficient prediction,average absolute error,reduced prediction accuracy,average prediction accuracy,electronic engineering,validation sample,simulationmodelingmetamodel validationsample size | Data mining,Computer science,Deterministic simulation,Square error,Mean squared error,Simulation modeling,Coefficient of determination,Sample size determination,Approximation error | Journal |
Volume | Issue | ISSN |
27 | 4 | 1435-5663 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Husam Hamad | 1 | 12 | 2.81 |