Table 4.
Results on Polifact for Machine Learning with basic parameters
| Classifier | ACC | PRE | REC | F1 | AUC | Training Time [s] | Test Time [s] |
|---|---|---|---|---|---|---|---|
| SGD | 58.3% | 61.4% | 72.2% | 66.4% | 59.9% | 0.08 | 0.04 |
| Naïve Bayes | 57.9% | 57.6% | 85.6% | 68.9% | 56.7% | 0.03 | 0.006 |
| Linear SVC | 55.6% | 61.0% | 65.0% | 62.9% | 58.3% | 0.31 | 0.004 |
| Random Forest | 55.1% | 57.4% | 77.7% | 66.0% | 55.3% | 3.59 | 0.08 |
| Logistic Regression | 59.6% | 62.3% | 74.9% | 68.0% | 60.2% | 0.25 | 0.005 |
| Nearest Neighbor | 53.3% | 57.5% | 63.3% | 60.3% | 54.5% | 0.021 | 4.20 |
| Decision Tree | 54.1% | 57.8% | 60.2% | 60.0% | 54.5% | 9.20 | 0.012 |
| Gradient Boost | 57.7% | 57.7% | 84.7% | 68.6% | 56.8% | 22.9 | 0.05 |
| Perceptron | 53.9% | 55.8% | 73.2% | 63.3% | 55.7% | 0.10 | 0.06 |
| Passive Aggressive | 55.2% | 58.5% | 75.5% | 65.9% | 58.0% | 0.11 | 0.05 |