Please discuss the differences between bagging and boosting and give an applicational example for each (please choose a specific method, such as AdaBoost or XGBoost, when discussing the boosting examples)
Please discuss the differences between bagging and boosting and give an applicational
example for each (please choose a specific method, such as AdaBoost or XGBoost, when
discussing the boosting examples)
Bagging and boosting are both methods used to improve the performance of machine learning models. Bagging is a method of training multiple models on different subsets of the data and then averaging the results. Boosting is a method of training multiple models on different subsets of the data and then weighting the results so that the models that perform better have a greater influence.
An example of bagging would be training a random forest model. This model would train multiple decision trees on different subsets of the data and then average the results. An example of boosting would be training an XGBoost model. This model would train multiple decision trees on different subsets of the data and then weight the results so that the trees that performed better have a greater influence.
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