Accuracy and AUC value of ML algorithms using three hyper parameter tuning techniques.
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posted on 2025-01-24, 18:42 authored by Alemu Birara Zemariam, Biruk Beletew Abate, Addis Wondmagegn Alamaw, Eyob shitie Lake, Gizachew Yilak, Mulat Ayele, Befkad Derese Tilahun, Habtamu Setegn NgusieAccuracy and AUC value of ML algorithms using three hyper parameter tuning techniques.
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unimproved toilet facilitytraditional stastical methodsrandom forest classifierpoor wealth indexml algorithms consideredmachine learning algorithmshealth survey dataset85 %) outperformed81 %, accuracy78 %, auc77 %, precision75 %, f13156 weighted samples2016 ethiopian demographicreach linear growthused contraceptive methodaccurately predict stuntingidentify best featuresidentified predictors couldassociation rule miningxlink "> stuntingpredicting stunting comparedxlink ">best rulepredict stuntingrelevant predictorsinvestigating growthvital indicatorusually focusedtop attributessupport toolsrural residencerelevant stakeholderspython softwarepotentially valuablenutrition statusmedia exposureindependent featureimportant interventiongiving emphasisfrequently associatedformal educationchronic undernutritionboruta algorithmavailable studiesapriori algorithm19 years
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