From: TNFPred: identifying tumor necrosis factors using hybrid features based on word embeddings
Classifier | Cross-validation data | |||
---|---|---|---|---|
 | Acc (%) | Spec (%) | Sen (%) | MCC |
SVM | 95.82 ± 1.67 | 97.59 ± 2.15 | 83.67 ± 7.45 | 0.83 ± 0.06 |
kNN | 77.33 ± 3.7 | 75.41 ± 3.98 | 100 ± 0 | 0.47 ± 0.03 |
RandomForest | 94.22 ± 2.3 | 94.20 ± 2.9 | 94 ± 8.43 | 0.75 ± 0.05 |
Naïve Bayes | 21.59 ± 10.62 | 14.76 ± 11.45 | 100 ± 0 | 0.09 ± 0.06 |
QuickRBF | 94.80 ± 1.52 | 99.81 ± 0.4 | 57.99 ± 14.25 | 0.72 ± 0.09 |
 | Independent data | |||
 | Acc (%) | Spec (%) | Sen (%) | MCC |
SVM | 96.49 ± 4.34 | 98 ± 5.27 | 85 ± 17.48 | 0.86 ± 0.13 |
kNN | 79.39 ± 8.9 | 78.01 ± 10.57 | 93.34 ± 14.04 | 0.47 ± 0.09 |
RandomForest | 97.28 ± 2.25 | 99 ± 2.26 | 80.01 ± 23.31 | 0.84 ± 0.14 |
Naïve Bayes | 19.09 ± 23.76 | 10.99 ± 26.15 | 100 ± 0 | 0.08 ± 0.17 |
QuickRBF | 94.12 ± 1.97 | 100 ± 0 | 50 ± 16.7 | 0.68 ± 0.13 |