What is the difference between bagging and boosting in ensemble learning? - Study24x7
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Ashish Verma
01 May 2024 10:49 AM study24x7 study24x7

What is the difference between bagging and boosting in ensemble learning?

A

Bagging involves training multiple models sequentially, while boosting trains them in parallel

B

Bagging aims to reduce model variance, while boosting focuses on reducing bias

C

Bagging combines predictions from multiple models, while boosting combines predictions by assigning weights to models 

D

Bagging is only applicable to decision tree models, while boosting can be applied to any algorithm

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