Method | MCC | F1 score | Accuracy | TP rate | TN rate | PR AUC | ROC AUC |
---|---|---|---|---|---|---|---|
Random forests | blue+0.384* | 0.547 | blue0.740* | 0.491 | 0.864 | 0.657 | blue0.800* |
Decision tree | +0.376 | blue0.554* | 0.737 | blue0.532* | 0.831 | 0.506 | 0.681 |
Gradient boosting | +0.367 | 0.527 | 0.738 | 0.477 | 0.860 | 0.594 | 0.754 |
Linear regression | +0.332 | 0.475 | 0.730 | 0.394 | 0.892 | 0.495 | 0.643 |
One rule | +0.319 | 0.465 | 0.729 | 0.383 | 0.892 | 0.482 | 0.637 |
Artificial neural network | +0.262 | 0.483 | 0.680 | 0.428 | 0.815 | blue0.750* | 0.559 |
Naïve bayes | +0.224 | 0.364 | 0.696 | 0.279 | 0.898 | 0.437 | 0.589 |
SVM radial | +0.159 | 0.182 | 0.690 | 0.122 | 0.967 | 0.587 | 0.749 |
SVM linear | +0.107 | 0.115 | 0.684 | 0.072 | blue0.981* | 0.594 | 0.754 |
k-nearest neighbors | -0.025 | 0.148 | 0.624 | 0.121 | 0.866 | 0.323 | 0.493 |