Performance tuning and error detection are the most important iterations for a machine learning system as it helps improve the performance of the system. Machine learning systems are considered to have optimal performance if the generalized function of the algorithm gives a low generalization error with a high probability. This is conventionally known as the probably approximately correct (PAC) theory.

To compute the generalization error, which is the accuracy of classification or the error in forecast of regression model, we use the metrics described in the following sections.