Table 4 Model performance after hyperparameter tuning
From: Explainable AI for enhanced accuracy in malaria diagnosis using ensemble machine learning models
Model | Accuracy S. | ROC AUC S. | MCC | Balanced A. | Cohen’s K. | Precision | Recall |
|---|---|---|---|---|---|---|---|
Random F. | 0.8195 | 0.8696 | 0.6374 | 0.8187 | 0.6374 | 0.8310 | 0.8310 |
AdaBoost | 0.5789 | 0.6336 | 0.1558 | 0.5780 | 0.1557 | 0.6087 | 0.5915 |
Gradient B. | 0.7293 | 0.8092 | 0.4559 | 0.7230 | 0.4505 | 0.7160 | 0.8169 |
XGBoost | 0.6917 | 0.7703 | 0.3812 | 0.6826 | 0.3710 | 0.6744 | 0.8169 |
CatBoost | 0.7293 | 0.7876 | 0.4551 | 0.7240 | 0.4517 | 0.7215 | 0.8028 |