MLCQ God Class Metric Based Evaluation Models
Comparison Table
| Model |
Accuracy |
Precision |
Recall |
F1-score |
| Logistic Regression |
0.8250 |
0.5799 |
0.6819 |
0.6228 |
| Random Forest |
0.8180 |
0.5656 |
0.6666 |
0.6079 |
| SVM |
0.8054 |
0.5319 |
0.8172 |
0.6400 |
| Decision Tree |
0.7784 |
0.4888 |
0.5654 |
0.5207 |
| Naive Bayes |
0.8329 |
0.6985 |
0.3743 |
0.4818 |
| Gradient Boosting |
0.8194 |
0.5609 |
0.7409 |
0.6347 |
| xgb |
0.8138 |
0.5619 |
0.5881 |
0.5723 |
Bar Plot
Evaluation Results
Logistic Regression
- Accuracy: 0.8250
- Precision: 0.5799
- Recall: 0.6819
- F1-score: 0.6228
Random Forest
- Accuracy: 0.8180
- Precision: 0.5656
- Recall: 0.6666
- F1-score: 0.6079
SVM
- Accuracy: 0.8054
- Precision: 0.5319
- Recall: 0.8172
- F1-score: 0.6400
Decision Tree
- Accuracy: 0.7784
- Precision: 0.4888
- Recall: 0.5654
- F1-score: 0.5207
Naive Bayes
- Accuracy: 0.8329
- Precision: 0.6985
- Recall: 0.3743
- F1-score: 0.4818
Gradient Boosting
- Accuracy: 0.8194
- Precision: 0.5609
- Recall: 0.7409
- F1-score: 0.6347
xgb
- Accuracy: 0.8138
- Precision: 0.5619
- Recall: 0.5881
- F1-score: 0.5723
