Model | AUC | Threshold | Accuracy | Kappa | Sensitivity | Specificity |
|---|
LM | 1 | 0.093 | 1 | 1 | 1 | 1 |
RF | 1 | 0.093 | 1 | 1 | 1 | 1 |
GBM | 1 | 0.093 | 1 | 1 | 1 | 1 |
DT | 1 | 0.093 | 1 | 1 | 1 | 1 |
NNET | 0.996 | 0.093 | 0.991 | 0.998 | 0.981 | 1 |
XGBOOST | 1 | 0.091 | 1 | 1 | 1 | 1 |
- AUC, area under the receiver operating characteristic curve; Threshold, optimal probability threshold for model predictions; Accuracy, overall accuracy of model predictions; Kappa, Cohen's Kappa statistic measuring prediction agreement; Sensitivity, model sensitivity in predicting presence; Specificity, model specificity in predicting absence; RF, random forest model; XGBOOST, extreme gradient boosting model; GBM, gradient boosting machine model; LM, logistic regression model; DT, decision tree model; NNET, neural network model
