Comparison of random prediction simulation, logistic regression, random forest, xgboost, balanced random forest, and balanced XGBoost models.

Comparison of random prediction simulation, logistic regression, random forest, xgboost, balanced random forest, and balanced XGBoost models.

<p>Comparison of random prediction simulation, logistic regression, random forest, xgboost, balanced random forest, and balanced XGBoost models.</p>

Enregistré dans:

Détails bibliographiques
Auteur principal:	Samantha Kanny (21673090) (author)
Autres auteurs:	Grisha Post (21673093) (author), Patricia Carbajales-Dale (784882) (author), William Cummings (21673096) (author), Janet Evatt (21673099) (author), Windsor Westbrook Sherrill (21673102) (author)
Publié:	2025
Sujets:	Science Policy Biological Sciences not elsewhere classified utilizing machine learning shapley additive explanations promoting weight loss nearest grocery store keep vulnerable individuals improve predictive modeling growing diabetic population enhance model interpretability educational attainment ), distressed communities index auc values ranging 53 &# 8211 19 &# 8211 whitney u tests various evaluation measures program evaluation measures highest auc value utilizing participant demographics slightly higher f machine learning models improving diabetes knowledge various analyses square tests management program highest rates spatial variables south carolina participants enrolled models tested life scores influential predictors hed ). healthcare settings gaining popularity future research findings underscore examine relationships drive time downsampling yielded descriptive statistics demographic factors comparative approach care behaviors 12 quality 1 scores
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Soyez le premier à ajouter un commentaire!