Here is the source code of a comparison study of ensemble learning method.
Abstract: We present an extensive empirical comparison between ninteen prototypical supervised ensemble learning algorithms, including Boosting, Bagging, Random Forests, Rotation Forests, Arc-X4, Class-Switching and their variants, as well as more recent techniques like Random Patches. These algorithms were compared against each other in terms of threshold, ranking/ordering and probability metrics over nineteen UCI benchmark data sets with binary labels. We also examine the influence of two base learners, CART and Extremely Randomized Trees, on the bias-variance decomposition and the effect of calibrating the models via Isotonic Regression on each performance metric. The selected data sets were already used in various empirical studies and cover different application domains. The source code and the detailed results of our study are publicly available.
Keywords: Ensemble learning, Empirical analysis, Binary classification