This paper evaluates the performance of Bagging and Boosting methods on 23 data sets using decision trees and neural networks. The study shows that Bagging generally produces more accurate classifiers than single classifiers, while Boosting can sometimes produce less accurate results, especially with neural networks. Boosting may overfit noisy data, leading to decreased performance. The results also indicate that most of the performance gain from ensembles comes from the first few classifiers, with Boosting decision trees showing significant improvements up to 25 classifiers. The study finds that ensemble methods are generally consistent in their effect on accuracy, but there is little correlation between neural networks and decision trees except for Boosting methods. The paper concludes that Bagging is more resilient to noise than Boosting, and that the performance of Boosting methods is at least partly dependent on the data set being examined. The results suggest that while Boosting can produce larger gains in accuracy, Bagging is generally more appropriate for most problems. The study also highlights the importance of considering the characteristics of the data set when choosing an ensemble method.This paper evaluates the performance of Bagging and Boosting methods on 23 data sets using decision trees and neural networks. The study shows that Bagging generally produces more accurate classifiers than single classifiers, while Boosting can sometimes produce less accurate results, especially with neural networks. Boosting may overfit noisy data, leading to decreased performance. The results also indicate that most of the performance gain from ensembles comes from the first few classifiers, with Boosting decision trees showing significant improvements up to 25 classifiers. The study finds that ensemble methods are generally consistent in their effect on accuracy, but there is little correlation between neural networks and decision trees except for Boosting methods. The paper concludes that Bagging is more resilient to noise than Boosting, and that the performance of Boosting methods is at least partly dependent on the data set being examined. The results suggest that while Boosting can produce larger gains in accuracy, Bagging is generally more appropriate for most problems. The study also highlights the importance of considering the characteristics of the data set when choosing an ensemble method.