Ensemble learning is the process by which multiple models, such as classifiers or experts, are strategically generated and combined to solve a particular computational intelligence problem. Ensemble learning is primarily used to improve the (classification, prediction, function approximation, etc.) performance of a model, or reduce the likelihood of an unfortunate selection of a poor one
Ensemble Learning uses several machine learning models built with different learning algorithms to improve the accuracy of the prediction. It basically averages the predictions of several models to get a better prediction.
It is like a set of models where we
- don’t want all models to be identical
- don’t want them to be different for the sake of it