Table 2 Metrics for each model.
Model | Precision | F1-Score | Accuracy |
|---|---|---|---|
Logistic regression | 0.9418 | 0.9195 | 0.9163 |
Naive Bayes | 0.9882 | 0.9881 | 0.9881 |
SVM | 0.9862 | 0.9855 | 0.9854 |
k - NN | 0.9823 | 0.9280 | 0.9818 |
Decision trees | 0.9891 | 0.9890 | 0.9890 |
Random forest | 0.9965 | 0.9963 | 0.9963 |
AdaBoost | 0.8840 | 0.8801 | 0.880 |
Gradient boosting | 0.9916 | 0.9909 | 0.9910 |
Extra trees | 0.9285 | 0.9268 | 0.9272 |
Multinomial NB | 0.9090 | 0.7513 | 0.7070 |
MLP | 0.9761 | 0.9746 | 0.9745 |
QDA | 0.9947 | 0.9945 | 0.9945 |
Average metrics | 0.9648 | 0.9490 | 0.9451 |
Compound ensemble | 0.9970 | 0.9970 | 0.9980 |
