Ranking forest model performance in ROC space for all balance features.

<p>By applying the Ranking Forest machine learning algorithm to the dataset under all conditions (eyes-open firm surface, eyes-closed firm surface, eyes-open foam surface, and eyes-closed foam surface), we evaluated the ROC space of the models for all balance features (both original parameters...

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Bibliographic Details
Main Author:	Yuqi Cheng (428511) (author)
Other Authors:	Dawei Wu (519682) (author), Ying Wu (19057) (author), Youcai Guo (20642726) (author), Xinze Cui (20642729) (author), Pengquan Zhang (20642732) (author), Jie Gao (10266) (author), Yanming Fu (1844281) (author), Xin Wang (91924) (author)
Published:	2025
Subjects:	Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified various athletic tasks test split method support vector machine remains unclear whether ranking forest yielded participant &# 8217 novel classification method hard support surfaces accessible measurement methods 13 elite athletes multidimensional approach appears xlink "> balance whereas cmci performed various indicators proposed roc results showed common approach balance differences balance ability balance abilities traditional time sports science significant challenge second trials postural control logistic regression highly suitable fully explain eyes open complex interactions classic train best overall analyses alone
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Ranking forest model performance in ROC space for all balance features.

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