Table 5 Results of various training–test splits to validate our model’s robustness using the optimized parameters of the RF classifier
From: Decision tree-based learning and laboratory data mining: an efficient approach to amebiasis testing
Training % | Testing % | Accuracy | Precision | Recall | F1 score |
|---|---|---|---|---|---|
10 | 90 | 0.908 | 0.914 | 0.908 | 0.910 |
20 | 80 | 0.925 | 0.925 | 0.925 | 0.925 |
30 | 70 | 0.936 | 0.938 | 0.936 | 0.937 |
40 | 60 | 0.927 | 0.929 | 0.927 | 0.928 |
50 | 50 | 0.933 | 0.934 | 0.933 | 0.933 |
60 | 40 | 0.942 | 0.942 | 0.942 | 0.942 |
70 | 30 | 0.957 | 0.957 | 0.957 | 0.957 |
80 | 20 | 0.977 | 0.979 | 0.977 | 0.977 |
90 | 10 | 0.981 | 0.981 | 0.981 | 0.981 |