Error calculation allows you to see how well a machine learning method is performing. One way of determining this performance is to calculate a numerical error. This number is sometimes a percent, however it can also be a score or distance. The goal is usually to minimize an error percent or distance, however th goal may be to minimize or maximize a score. Encog supports the following error calculation methods.
- Sum of Squares Error (ESS)
- Root Mean Square Error (RMS)
- Mean Square Error (MSE) (default)
- SOM Error (Euclidean Distance Error)
Changing the Error Calculation Method in Encog
The error calculation method can be changed in Encog. Typically, you will choose between RMS and MSE. The other methods are used for more specialized situations. As of Encog 3.0, the default is MSE. MSE typically gives the best performance.
ErrorCalculation.Mode = ErrorCalculationMode.MSE;