A simple definition of ensemble learning is given by Zhihua Zhou: Ensemble learning is a machine learning paradigm where multiple learners are trained to solve the same problem. In contrast to ordinary machine learning approaches which try to learn one hypothesis from training data, ensemble methods try to construct a set of hypotheses and combine them to use.
- Provide better performance than single base learner.
Bagging and Boosting
The difference between bagging and boosting can be found here.