In this section we compare our technique of extracting a sure correct (SC) DT with 

![0_image_0.png](0_image_0.png) MAP, and the maximum a posterior weight (MAPW). The comparison is made in terms of misclassification within 5 fold cross-validation. The misclassification rates of the above three techniques: SC, MAP, and MAPW are shown in Fig. 5. The left side plot shows the misclassification rates of the single DTs on the test data, and the right side plot shows its sizes.

In theory, the Bayesian averaging technique should provide lower misclassification rates than any other single DTs selected by the SC, MAP, and MAPW techniques. On the first run, we can observe that all the single DTs perform worse than the Bayesian ensemble of DTs which has misclassified 14.3% of the test data. 

Comparing the misclassification rates of the SC, MAP, and MAPW shown in Fig. 

5, we can see that the suggested SC technique more often out-performs the other two techniques, that is, the SC technique out-performs the MAP and MAPW techniques on the 4 runs. 

Comparing the DT sizes on the right side plot, we can see that the SC technique has extracted shorter DTs than the MAP technique in 4 runs. At the same time, comparing the sizes of the SC and MAPW DTs, we can see that the SC technique has extracted shorter DTs in 2 runs only. 