Bagging consists of fitting several base models on different bootstrap samples and building an ensemble model that averages the results of these base learners.

Bagging consists of fitting several base models on different bootstrap samples and building an ensemble model that averages the results of these base learners.

<p>Bagging consists of fitting several base models on different bootstrap samples and building an ensemble model that averages the results of these base learners.</p>

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Bibliographic Details
Main Author:	Shikun Chen (14625352) (author)
Other Authors:	Wenlong Zheng (14140) (author)
Published:	2025
Subjects:	Biological Sciences not elsewhere classified Information Systems not elsewhere classified specific domain knowledge relative error rates overall prediction quality improve prediction accuracy existing methods across demonstrate higher accuracy continuous numerical targets base model based boosting prediction performance based weighting function optimizing ensemble performance uniform weighting approach rrmse voting regressor voting regressor smart weighting ensemble models widely used thereby enhancing significant advancement several state results show method gives leaving room different datasets assigns weights also provides adaptable method &# 8217
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