Ensemble methods’ performances when PCA feature selection method and intensities are used.

Ensemble methods’ performances when PCA feature selection method and intensities are used.

<p>Top left: RF. Top right: ADA. Bottom left: XG. Bottom right: GB.</p>

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Bibliographic Details
Main Author:	Matthew Yit Hang Yeow (20721206) (author)
Other Authors:	Chun Yong Chong (20721209) (author), Mei Kuan Lim (20721212) (author), Yuen Yee Yen (13141043) (author)
Published:	2025
Subjects:	Biochemistry Cell Biology Cancer Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified public undocumented api leveraging machine learning existing github projects approach uses cross identifying suitable software identifying key metrics enabling future research measure software reuse estimate software reuse potential reuse candidates rank maven artifacts quality software components ml classification models software reuse reuse potential software metrics maven artifacts software production train classification research aims trained models reusing artifacts regression models selecting high ridge regression reduce costs performing model objective methods level pua increase efficiency improve maintainability ground truth findings suggest essential practice case study 19 %. 17 %.
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