Also known as: Ensemble Methods
Ensemble learning is a machine learning approach that combines multiple models or algorithms to achieve better predictive performance than individual components alone. Common examples include bagging, boosting, random forests, and stacked models.
The use of multiple machine learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone.