Bagging or Boosting?
Katherine wants to use an ensemble model to process her dataset.
There’s only one question for her to answer: Should she use bagging or boosting?
Both techniques have different advantages and disadvantages, and Katherina wants to ensure she evaluates them correctly before committing to one solution.
Which of the following statements are true about bagging and boosting?
Bagging trains individual models sequentially, using the results from the previous model to inform the selection of training samples.
Boosting trains individual models sequentially, using the results from the previous model to inform the selection of training samples.
Bagging trains a group of models, each using a subset of data selected randomly with replacement from the original dataset.
Each model receives equal weight in bagging to compute the final prediction while boosting uses some way of weighing each model.