A Comparative Evaluation use Bagging and Boosting Ensemble Classifiers
Hanae Aoulad Ali, Chrayah Mohamed, Abdelhamid Bouzidi, Nabil Ourdani, Taha El Alami
Abstract
In recent years, ensemble learning has sparked a lot of interest in the fields of machine learning. In a variety of issue areas domains and real-world applications, recently ensemble learning approach get a lot attention to provide results. Ensemble learning reduces overall variance by combining the output of numerous classifiers or a group of base learners. When compared to a single classifier or single basis learner, combining numerous Classifiers or a collection of base learners improves accuracy significantly. This research is aimed at comparison of two sort of ensemble learning approaches used in machine learning. The Extra Trees classifier had been the most accurate, with a score of accuracy of 90 %