Specifically in this notebook, we will take a look at the following techniques: BaggingClassifier RandomForestClassifier AdaBoostClassifier Another really useful guide for ensemble methods can be found in the documentation here. These ensemble methods use a combination of techniques you have seen throughout this lesson: Bootstrap the data passed through a learner (bagging). Subset the features used for a learner (combined with bagging signifies the two random components of random forests). Ensemble learners together in a way that allows those that perform best in certain areas to create the largest impact (boosting).