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RF Algorithm

CL Chanjung Lee
BJ Brian Jo
HW Hyunki Woo
YI Yoori Im
RP Rae Woong Park
CP ChulHyoung Park
r rk.ca.uoja@satirev
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RF is a tree-based ensemble model capable of both classification and regression and selects the most appropriate forest model by collecting the results of randomly generated independent decision trees [11]. Bagging-based training data inputted to the tree provides model diversity, and the randomness of variable combinations constituting the tree can prevent model noise and the risk of overfitting. The fact that RF is less sensitive to missing values than other algorithms is also an advantage.

Copyright and License information:  ©2022-12-22 Chanjung Lee, Brian Jo, Hyunki Woo, Yoori Im, Rae Woong Park, ChulHyoung Park. Originally published in JMIR AI (https://ai.jmir.org), 22.12.2022.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR AI, is properly cited. The complete bibliographic information, a link to the original publication on https://www.ai.jmir.org/, as well as this copyright and license information must be included.

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