We present an extensive empirical comparison between twenty 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 datasets with binary labels. We also examine the influence of two base learners, CART and Extremely Randomized Trees, and the effect of calibrating the models via Isotonic Regression on each performance metric. The selected datasets were already used in various empirical studies and cover different application domains. The experimental analysis was restricted to the hundred most relevant features according to the SNR filter method with a view to dramatically reducing the computational burden involved by the simulation. The source code and the detailed results of our study are publicly available.