Ensemble Learning - Bagging, Boosting, and Stacking explained in 4 minutes!

In this video, we go through a high level overview of ensemble learning methods. We discuss bagging (bootstrap aggregating), boosting (such as AdaBoost and Gradient Boosting), and stacking (stacked ensembles). We also go over the difference between bagged decision trees and the random forest algorithm. Ensemble learning models are often the top performers in Data Science competitions. Slides are available here: https://github.com/melissavanbussel/YouTube-Tutorials/tree/main/EnsembleLearning