In this regard, the classification is performed using the naive Bayes classifier, which is a special case of the Bayes classifier that yields the minimum misclassification risk among all statistical classification rules [51].
The purpose of the learning is to build a model with minimum misclassification total costs.
Step 9. Select the best model according to minimum misclassification error and record it into cross-validation consistency (CVC).
This is closely related to Bayes classifier, which is an optimal classifier with the minimum misclassification risk.
Select the best model according to minimum misclassification error and record it into cross-validation consistency (CVC).
The key ideas of the method are the Maximum Relevance criterion and Minimum Redundancy criterion as meant by its name.
Therefore, the minimum conditional MSE criterion reduces to the MSE criterion when the CSI mismatch vanishes.
The weights are determined by optimising a misclassification rate criterion, however, instead of a least-squares criterion as in linear discriminant analysis (Ramaswamy et al, 2001).
The minimum criterion for node split was defined as 0.05, and the minimum bucket was defined as forty patients.
minimum description length criterion.
