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>

محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Alemu Birara Zemariam (17540938) (author)
مؤلفون آخرون: 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)
منشور في: 2025
الموضوعات:
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
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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
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