Table 4 A comparative table of performance metrics for different models.
From: Fault classification in the architecture of virtual machine using deep learning
Classifier code | Classifier | Accuracy score | Precision score | Recall score | F1 score |
|---|---|---|---|---|---|
0 | AdaBoostClassifier | 0.687 | 0.493 | 0.588 | 0.515 |
1 | KNeighborsClassifier | 0.760 | 0.605 | 0.719 | 0.642 |
2 | SVC | 0.648 | 0.333 | 0.216 | 0.262 |
3 | RandomForestClassifier | 0.969 | 0.952 | 0.960 | 0.956 |
4 | DecisionTreeClassifier | 0.969 | 0.938 | 0.975 | 0.955 |
5 | GaussianNB | 0.544 | 0.447 | 0.481 | 0.430 |
6 | LogisticRegression | 0.642 | 0.362 | 0.415 | 0.335 |
7 | GradientBoostingClassifier | 0.739 | 0.578 | 0.688 | 0.612 |
8 | Proposed model | 0.983 | 0.983 | 0.975 | 0.979 |
