Which of the following is a fundamental difference between bagging and boosting?
A) Bagging is used for supervised learning. Boosting is used with unsupervised clustering.
B) Bagging gives varying weights to training instances. Boosting gives equal weight to all training instances.
C) Bagging does not take the performance of previously built models into account when building a new model. With boosting each new model is built based upon the results of previous models.
D) With boosting, each model has an equal weight in the classification of new instances. With bagging, individual models are given varying weights.

Question

Quizplus · Accepted Answer

The fundamental difference between bagging and boosting is that bagging does not take the performance of previously built models into account when building a new model, whereas with boosting each new model is built based upon the results of previous models. Option A is incorrect because both bagging and boosting are used for supervised learning. Option B is incorrect because bagging can also give equal weights to all training instances. Option D is incorrect because in bagging, all models have equal weight in the classification of new instances.

Which of the Following Is a Fundamental Difference Between Bagging