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Table 1 Average training, validation and test AUCs for the three SuRFR models run on the cross-validation datasets

From: SuRFing the genomics wave: an R package for prioritising SNPs by functionality

Model Training AUC Validation AUC Test AUC Performance error Gerenalisation error
ALL 0.944 0.944 0.909 0.000 0.035
DM 0.976 0.976 0.956 0.000 0.020
DFP 0.912 0.908 0.897 0.004 0.013
  1. The AUCs and error rates from cross-validation for the three SuRFR models. Column 1 shows the three models (ALL, DM, DFP). Columns 2 and 3 show the average training AUCs and validation AUCs, respectively, for each of the three models from the 10-fold cross-validation analysis. The performance error (column 5) shows that the difference between the training and validation AUCs is small. Column 4 shows the average test AUCs achieved by each of the three models run on the hold-out datasets. The low gerenalisation errors in column 6 and the AUCs from the test datasets show that SuRFR is likely to gerenalise and perform equally well on novel data.