MLCQ God Class Token Based Evaluation Models
Comparison Table
| Model |
Accuracy |
Precision |
Recall |
F1-score |
| Logistic Regression |
0.7225 |
0.3785 |
0.4830 |
0.4135 |
| Random Forest |
0.8268 |
0.6810 |
0.3634 |
0.4707 |
| SVM |
0.5754 |
0.2965 |
0.7089 |
0.4143 |
| Decision Tree |
0.7654 |
0.4526 |
0.4303 |
0.4376 |
| Naive Bayes |
0.5833 |
0.2972 |
0.6953 |
0.4134 |
| Gradient Boosting |
0.8231 |
0.6190 |
0.4701 |
0.5309 |
| xgb |
0.8185 |
0.6119 |
0.4525 |
0.5152 |
Bar Plot
Evaluation Results
Logistic Regression
- Accuracy: 0.7225
- Precision: 0.3785
- Recall: 0.4830
- F1-score: 0.4135
Random Forest
- Accuracy: 0.8268
- Precision: 0.6810
- Recall: 0.3634
- F1-score: 0.4707
SVM
- Accuracy: 0.5754
- Precision: 0.2965
- Recall: 0.7089
- F1-score: 0.4143
Decision Tree
- Accuracy: 0.7654
- Precision: 0.4526
- Recall: 0.4303
- F1-score: 0.4376
Naive Bayes
- Accuracy: 0.5833
- Precision: 0.2972
- Recall: 0.6953
- F1-score: 0.4134
Gradient Boosting
- Accuracy: 0.8231
- Precision: 0.6190
- Recall: 0.4701
- F1-score: 0.5309
xgb
- Accuracy: 0.8185
- Precision: 0.6119
- Recall: 0.4525
- F1-score: 0.5152
