| Model | Accuracy | Recall | Precision | \( F_1 \) |
| Nearest Neighbours | \( 0.94 \) | \( 0.54 \) | \( 0.72 \) | \( 0.31 \) |
| RBF SVM | \( 0.94 \) | \( 0.42 \) | \( 0.83 \) | \( 0.28 \) |
| Gaussian Process | \( 0.94 \) | \( 0.46 \) | \( 0.85 \) | \( 0.30 \) |
| Gaussian Naive Bayes | \( 0.91 \) | \( 0.42 \) | \( 0.56 \) | \( 0.24 \) |
| Decision Tree | \( 0.94 \) | \( 0.42 \) | \( 0.83 \) | \( 0.28 \) |
| Random Forest | \( 0.93 \) | \( 0.29 \) | \( 1.00 \) | \( 0.22 \) |
| Multi-layer Perceptron | \( 0.94 \) | \( 0.38 \) | \( 0.9 \) | \( 0.27 \) |
| AdaBoost | \( 0.94 \) | \( 0.54 \) | \( 0.81 \) | \( 0.32 \) |
| QDA | \( 0.92 \) | \( 0.46 \) | \( 0.61 \) | \( 0.26 \) |
| Variational QBM | \( \mathbf{0.95} \) | \( \mathbf{0.63} \) | \( \mathbf{0.83} \) | \( \mathbf{0.36} \) |
Performance measures for scikit-learn standard classifiers, as well as the trained QBM.