Model | Tool | AUC | CA | F1 | Precision | Recall | Specificity | Support |
---|---|---|---|---|---|---|---|---|
SVM | Orange | 0.746 | 0.744 | 0.698 | 0.736 | 0.744 | 0.462 | – |
Sklearn | 0.615 | 0.6 | 0.59 | 0.62 | – | 39 | ||
Random | Orange | 0.746 | 0.725 | 0.707 | 0.705 | 0.725 | 0.525 | – |
Forest | Sklearn | 0.692 | 0.7 | 0.73 | 0.69 | – | 39 | |
Logistic | Orange | 0.796 | 0.769 | 0.752 | 0.757 | 0.769 | 0.579 | – |
Regression | Sklearn | 0.769 | 0.77 | 0.76 | 0.77 | – | 39 | |
AdaBoost | Orange | 0.744 | 0.788 | 0.785 | 0.783 | 0.788 | 0.683 | – |
Sklearn | 0.79 | 0.79 | 0.79 | 0.79 | – | 39 | ||
Naïve | Orange | 0.753 | 0.763 | 0.763 | 0.763 | 0.763 | 0.672 | – |
Bayes | Sklearn | 0.785 | 0.78 | 0.79 | 0.78 | – | 39 | |
Neural | Orange | 0.767 | 0.75 | 0.747 | 0.745 | 0.75 | 0.631 | – |
Network | Sklearn | 0.692 | 0.57 | 0.48 | 0.69 | – | 39 | |
kNN | Orange | 0.646 | 0.669 | 0.627 | 0.618 | 0.669 | 0.394 | – |
Sklearn | 0.615 | 0.6 | 0.59 | 0.62 | – | 39 | ||
CN2 rule | Orange | 0.721 | 0.694 | 0.696 | 0.699 | 0.694 | 0.595 | – |
Inducer | Sklearn | – | – | – | – | |||
Decision | Orange | 0.7 | 0.713 | 0.717 | 0.723 | 0.713 | 0.639 | – |
Tree | Sklearn | 0.692 | 0.7 | 0.71 | 0.69 | – | 39 | |
Quadratic | Orange | – | – | – | – | |||
Classifier | Sklearn | 0.744 | 0.7 | 0.75 | 0.74 | – | 39 |