The study utilized 11 publicly available MDP NASA software defect datasets. Two ensembles are classified as bagging ensembles: Random Forest and Extra Trees, while the other five ensembles are boosting ensembles: Ada boost, Gradient Boosting, Hist Gradient Boosting, XGBoost and CatBoost. In this paper, we will empirically investigate the prediction performance of seven Tree-based ensembles in defect prediction. Recently, many Tree-based ensembles have been proposed in the literature, and their prediction capabilities were not investigated in defect prediction. In machine learning, ensemble learning has been proven to improve the prediction performance over individual machine learning models. Accurate prediction of software defects assists software engineers in guiding software quality assurance activities. Software defect prediction is an active research area in software engineering. Hamoud Aljamaanâ and Amal Alazbaâ (King Fahd University of Petroleum and Minerals, Saudi Arabia King Saud University, Saudi Arabia) Software Defect Prediction using Tree-Based Ensembles PROMISE '20: "Software Defect Prediction. 16th ACM International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE 2020),
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