Comparison of the performance evaluation of Random Forest results for binary classification on both balanced and unbalanced data set.

<p>Comparison of the performance evaluation of Random Forest results for binary classification on both balanced and unbalanced data set.</p>

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Bibliographic Details
Main Author:	Toheeb Salahudeen (21368040) (author)
Other Authors:	Maher Maalouf (6318215) (author), Ibrahim (Abe) M. Elfadel (21368043) (author), Herbert F. Jelinek (7039787) (author)
Published:	2025
Subjects:	Cell Biology Biotechnology Science Policy Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified machine learning present machine learning models global mental health 56 %, respectively 26 %, respectively 1 %, respectively unbalanced data sets aid clinical assessment multiclass classification scenarios including random forest random forest clinical indicators three classes significant challenge shedding light rf achieved recent advances precise relationship often intertwined mitochondrial peptides five classes findings underscore f1 score diabetes mellitus comorbid conditions balanced data
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