New Amazon Data Scientist Interview Practice Problems for 2021

Bagging, also known as bootstrap aggregating, is the process in which multiple models of the same learning algorithm are trained with bootstrapped samples of the original dataset. Then, like the random forest example above, a vote is taken on all of the models' outputs. Boosting is a variation of bagging where each individual model is built sequentially, iterating over the previous one. Specifically, any data points that are falsely classified by the previous model is emphasized in the following model. This is done to improve the overall accuracy of the model.