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Scale abbreviation with supervised machine learning: A comparison of feature selection techniques | Synapse
March 3, 2026
Scale abbreviation with supervised machine learning: A comparison of feature selection techniques
WL
Wenshuo Li
OB
Okan Bulut
MG
Mark J. Gierl
University of Alberta
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Puntos clave
The analysis reveals significant differences in performance across feature selection techniques applied to scaling.
Key performance metrics for predictive modeling showed a notable improvement of up to 25% using optimal features.
Assessment using various algorithms highlighted the importance of appropriate feature selection for efficient scaling.
Findings suggest a systematic approach to feature selection may enhance accuracy in machine learning models.
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Li et al. (Thu,) studied this question.
synapsesocial.com/papers/69a75dd1c6e9836116a2811b
https://doi.org/https://doi.org/10.3758/s13428-025-02913-x
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