Table 4. Model selection criteria and performance metrics for the models selected for each modeling algorithm used in the ensemble model of Ixodes pacificus distribution.
Model selection | GLMa | MARSb | Maxenta | RFa | ||||
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Performance metric | Test split | Train | Test split | Train | Test split | Train | Test split | Train |
AUCc | 0.95 | 0.98 | 0.94 | 0.97 | 0.94 | 0.97 | 0.95 | 0.93 |
Percent correctly classified | 89.9 | 90.8 | 89.2 | 92.4 | 89.1 | 92.0 | 92.5 | 89.5 |
Sensitivity | 0.91 | 0.91 | 0.88 | 0.93 | 0.90 | 0.92 | 0.89 | 0.89 |
Specificity | 0.89 | 0.91 | 0.90 | 0.92 | 0.89 | 0.92 | 0.95 | 0.90 |
Mean threshold | 0.35 | 0.26 | 0.33 | 0.31 | 0.30 | 0.27 | 0.49 | 0.42 |
Candidate variables (predictor set 1): Bio6, Bio19, Bio3, Bio18, Bio8, Bio9, Bio2, GDD12Cum, Bio5, PercForest, Vp7.
Candidate variables (predictor set 2): Bio15, Bio19, Bio1, Bio17, Bio8, Vp10, GDD2Cum, PercForest.
Area under the (ROC) curve.
GLM, generalized linear model; MARS, multivariate adaptive regression spline; Maxent, maximum entropy; RF, random forest.