Table 2 Performance comparison of four algorithms on training and testing datasets.
From: Constructing a predictive model for acute mastitis in lactating women based on machine learning
Machine learning models | Training accuracy | Training sensitivity | Training specificity | Training F1 score | Training AUROC | Testing accuracy | Testing sensitivity | Testing specificity | Testing F1 score | Testing AUROC |
|---|---|---|---|---|---|---|---|---|---|---|
Logistic regression | 0.859 | 0.863 | 0.855 | 0.870 | 0.904 | 0.809 | 0.831 | 0.781 | 0.827 | 0.852 |
Naive bayes | 0.787 | 0.897 | 0.655 | 0.822 | 0.897 | 0.691 | 0.843 | 0.507 | 0.750 | 0.826 |
XGBoost | 0.867 | 0.832 | 0.909 | 0.873 | 0.927 | 0.796 | 0.764 | 0.836 | 0.805 | 0.852 |
Multilayer perceptron | 0.890 | 0.858 | 0.929 | 0.895 | 0.906 | 0.840 | 0.820 | 0.863 | 0.849 | 0.898 |
