Ensemble learning methods seek to improve the predictive performance for a single task by combining multiple learned systems (called base learners) into a more powerful model. On the other hand, meta-learning aims at improving predictive performance by learning how to adapt a model for different related tasks. In some cases, meta-learning can be used in combination with ensemble strategies, e.g., meta-learning how to best combine multiple base learners.