Table 3.
Results on LIAR for Machine Learning with basic parameters
| Classifier | ACC | PRE | REC | F1 | AUC | Training Time [s] | Test Time [s] |
|---|---|---|---|---|---|---|---|
| SGD | 62.0% | 62.7% | 73.8% | 67.8% | 61.0% | 0.06 | 0.03 |
| Naïve Bayes | 60.4% | 59.1% | 87.1% | 70.4% | 58.0% | 0.02 | 0.004 |
| Linear SVC | 60.0% | 62.3% | 66.2% | 64.2% | 59.5% | 0.27 | 0.002 |
| Random Forest | 58.6% | 58.8% | 79.0% | 67.4% | 56.8% | 3.57 | 0.06 |
| Logistic Regression | 62.7% | 63.1% | 75.8% | 68.4% | 61.6% | 0.22 | 0.002 |
| Nearest Neighbor | 56.3% | 58.8% | 64.6% | 61.6% | 55.6% | 0.019 | 4.17 |
| Decision Tree | 56.2% | 59.2% | 61.6% | 60.4% | 55.8% | 9.18 | 0.009 |
| Gradient Boost | 60.3% | 59.2% | 86.0% | 70.1% | 58.0% | 22.7 | 0.02 |
| Perceptron | 57.8% | 57.2% | 74.9% | 63.8% | 57.1% | 0.08 | 0.05 |
| Passive Aggressive | 59.3% | 59.9% | 76.8% | 70.2% | 59.1% | 0.09 | 0.04 |