Comparison among Methods of Ensemble Learning

Ensemble learning refers to a collection of methods that learn a target function by training a number of individual learners and combining their predictions. We explore four popular methods (bagging, boosting, stacking and random forest) of combining their outputs, for classification and training time and regression problems. Following this, experimental evaluations are performed on UCI datasets.