Table 2 F1 score for Algorithms on the test, left-out and train samples
From: Use of machine learning techniques to identify HIV predictors for screening in sub-Saharan Africa
samples | XGBoost | KNN | SVM | RF | EN | LGBM |
|---|---|---|---|---|---|---|
males test | 0.90 | 0.85 | 0.87 | 0.87 | 0.84 | 0.86 |
females test | 0.92 | 0.88 | 0.89 | 0.89 | 0.90 | 0.88 |
males left-out | 0.83 | 0.81 | 0.79 | 0.79 | 0.72 | 0.81 |
females left-out | 0.85 | 0.85 | 0.86 | 0.86 | 0.76 | 0.87 |
males train | 0.90 | 0.85 | 0.86 | 0.91 | 0.83 | 0.86 |
females train | 0.91 | 0.87 | 0.89 | 0.92 | 0.88 | 0.88 |