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Table 2 CM presents the confusion matrix of a run. The first row captures the number of true positives and false positives. The second row presents the number of false negatives and true negatives. The test results in % right of CM and algorithmic parameters used for the classification of the data determined with Weka

From: Application of an interpretable classification model on Early Folding Residues during protein folding

CM   CA PR RE F1 auROC
Naive Bayes
187 195 72.8 23.9 38.8 29.6 70.9
195 2190      
Random Forest
192 290 82.1 39.6 39.8 39.7 64.7
293 2491      
Support Vector Machine
134 348 87.0 63.2 27.8 38.6 62.5
78 2706      
GMLVQ with 1 prototype per class
Run 1
320 162 69.6 27.8 66.4 39.2 67.7
830 1954      
Run 2
351 162 68.7 28.3 72.8 40.7 73.7
890 1954      
Run 3
348 134 68.6 28.1 72.2 40.4 76.6
891 1893      
GMLVQ with 5 prototype per class
Run 4
187 295 77.4 29.7 38.8 33.6 69.4
443 2341      
Run 5
288 194 69.0 26.0 59.8 36.2 70.5
819 1965      
Run 6
274 208 70.3 26.4 56.8 36.1 70.3
763 2021      
  1. Additionally, we marked the best values for the single evaluation measured bold. If not stated otherwise, default setup was used. SVM with RBF-kernel (σ=5) which results in 1193 number of support vectors. Weights for weighted accuracy: 0.75 and 0.25. F β-measure with β=1 (F1)