Ensemble learning is a machine learning technique that trains multiple learners with the same data with each learner using a different learning algorithm. Ensemble learning often outperforms a single learning algorithm. This is particularly true when the ensemble includes diverse algorithms that each take a completely different approach.
Each learning algorithm is referred to as a base learner. The algorithm used to combine results is known as the ensemble. As an example, consider a task such as recognizing things in photos. An ensemble learning approach might give the same set of millions of labeled photos to dozens of learning algorithms. Each algorithm develops its own hypotheses to explain photos. These hypotheses are then combined by the ensemble to develop an image recognition capability.
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