MLCQ God Class Tree Based Evaluation Models
Comparison Table
| Model |
Accuracy |
Precision |
Recall |
F1-score |
| Logistic Regression |
0.6285 |
0.3162 |
0.6221 |
0.4159 |
| Random Forest |
0.7952 |
0.5290 |
0.4214 |
0.4661 |
| SVM |
0.7425 |
0.4364 |
0.7161 |
0.5381 |
| Decision Tree |
0.6965 |
0.3497 |
0.4733 |
0.3994 |
| Naive Bayes |
0.6355 |
0.3453 |
0.7858 |
0.4772 |
| Gradient Boosting |
0.7523 |
0.4432 |
0.6128 |
0.5111 |
| xgb |
0.7826 |
0.4936 |
0.4877 |
0.4865 |
Bar Plot
Evaluation Results
Logistic Regression
- Accuracy: 0.6285
- Precision: 0.3162
- Recall: 0.6221
- F1-score: 0.4159
Random Forest
- Accuracy: 0.7952
- Precision: 0.5290
- Recall: 0.4214
- F1-score: 0.4661
SVM
- Accuracy: 0.7425
- Precision: 0.4364
- Recall: 0.7161
- F1-score: 0.5381
Decision Tree
- Accuracy: 0.6965
- Precision: 0.3497
- Recall: 0.4733
- F1-score: 0.3994
Naive Bayes
- Accuracy: 0.6355
- Precision: 0.3453
- Recall: 0.7858
- F1-score: 0.4772
Gradient Boosting
- Accuracy: 0.7523
- Precision: 0.4432
- Recall: 0.6128
- F1-score: 0.5111
xgb
- Accuracy: 0.7826
- Precision: 0.4936
- Recall: 0.4877
- F1-score: 0.4865
