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Machine | Learning Quiz Questions and Answers | Question 29
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What is the difference between bagging and boosting in ensemble learning?
Bagging increases model diversity, boosting decreases it
Bagging trains models sequentially, boosting trains them in parallel
Bagging combines predictions using voting, boosting combines predictions using weighted averaging
Bagging trains each model independently, boosting focuses on examples misclassified by previous models
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