A collection of models trained independently whose predictions are averaged or aggregated. In many cases, an ensemble produces better predictions than a single model. For example, a random forest is an ensemble built from multiple decision trees. Note that not all decision forests are ensembles. See Random Forest in Machine Learning Crash Course for more information.
A collection of models trained independently whose inferences are averaged or aggregated. In many cases, an ensemble produces better inferences than a single model. For example, a random forest is an ensemble built from multiple decision trees. Note that not all decision forests are ensembles.