Type of evaluation | ML algorithm | Weighted average among all classes | |||||
---|---|---|---|---|---|---|---|
Accuracy | Precision | Recall | F-Measure | MCC | AUC | ||
Using a test set | MLP | 0.9444 | 0.946 | 0.944 | 0.944 | 0.891 | 0.969 |
SMO | 0.8107 | 0.815 | 0.811 | 0.810 | 0.625 | 0.810 | |
RF | 0.9542 | 0.955 | 0.954 | 0.954 | 0.909 | 0.988 | |
10-fold cross validation | MLP | 0.9369 | 0.937 | 0.937 | 0.937 | 0.871 | 0.972 |
SMO | 0.8568 | 0.861 | 0.857 | 0.855 | 0.709 | 0.844 | |
RF | 0.9601 | 0.960 | 0.960 | 0.960 | 0.919 | 0.992 | |
Leave-one-out | MLP | 0.944 | 0.944 | 0.944 | 0.944 | 0.886 | 0.975 |
SMO | 85.597 | 0.860 | 0.856 | 0.854 | 0.707 | 0.843 | |
RF | 96.228 | 0.963 | 0.962 | 0.962 | 0.923 | 0.992 | |
Mean performance | MLP | 0.9420 | 0.9430 | 0.9417 | 0.9417 | 0.8843 | 0.9700 |
SMO | 0.8411 | 0.8433 | 0.8413 | 0.8339 | 0.6803 | 0.8323 | |
RF | 0.9588 | 0.9533 | 0.9586 | 0.9586 | 0.917 | 0.9906 |