Accuracy and AUC value of ML algorithms using three hyper parameter tuning techniques.

Accuracy and AUC value of ML algorithms using three hyper parameter tuning techniques.

<p>Accuracy and AUC value of ML algorithms using three hyper parameter tuning techniques.</p>

Saved in:

Bibliographic Details
Main Author:	Alemu Birara Zemariam (17540938) (author)
Other Authors:	Biruk Beletew Abate (9339227) (author), Addis Wondmagegn Alamaw (18805678) (author), Eyob shitie Lake (20614334) (author), Gizachew Yilak (8436942) (author), Mulat Ayele (16559652) (author), Befkad Derese Tilahun (17642806) (author), Habtamu Setegn Ngusie (12178327) (author)
Published:	2025
Subjects:	Biotechnology Cancer Science Policy Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified unimproved toilet facility traditional stastical methods random forest classifier poor wealth index ml algorithms considered machine learning algorithms health survey dataset 85 %) outperformed 81 %, accuracy 78 %, auc 77 %, precision 75 %, f1 3156 weighted samples 2016 ethiopian demographic reach linear growth used contraceptive method accurately predict stunting identify best features identified predictors could association rule mining xlink "> stunting predicting stunting compared xlink "> best rule predict stunting relevant predictors investigating growth vital indicator usually focused top attributes support tools rural residence relevant stakeholders python software potentially valuable nutrition status media exposure independent feature important intervention giving emphasis frequently associated formal education chronic undernutrition boruta algorithm available studies apriori algorithm 19 years
Tags:	Add Tag No Tags, Be the first to tag this record!

Cannot write session to /tmp/vufind_sessions/sess_i3948pp64cnp0ac01uhi052mnu