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Table 2 Comparison of the prediction performance of different models

From: Prediction of Oncomelania hupensis distribution in association with climate change using machine learning models

Model

AUC (95% CI)

Sensitivity

Specificity

Kappa

RF

0.991 (0.989–0.993)

0.982

0.995

0.942

GBM

0.983 (0.973–0.993)

0.981

0.991

0.932

XGB

0.980 (0.966–0.994)

0.962

0.945

0.889

KNN

0.979 (0.962–0.996)

0.975

0.849

0.789

GAM

0.973 (0.946–0.999)

0.868

0.962

0.842

NN

0.969 (0.932–0.987)

0.959

0.940

0.917

SVM

0.896 (0.874–0.920)

0.679

0.981

0.724

CART

0.884 (0.863–0.905)

0.922

0.943

0.829

  1. RF, random forest; GBM, generalized boosted model; XGB, eXtreme Gradient Boosting; KNN, k-nearest neighbors; GAM, generalized additive model; NN, neural network; SVM, support vector machine; CART, classification and regression trees
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Parasites & Vectors

ISSN: 1756-3305

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