Model performance in ROC space across different algorithms and task conditions.

<p>Performance of models in ROC space for datasets under all conditions (firm surface with eyes open and eyes closed, foam surface with eyes open and eyes closed), using all data processing methods (original time domain and CMCI method), and all classification methods (Bayesian, Ranking Tree,...

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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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Model performance in ROC space across different algorithms and task conditions.

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