Table 3 Confusion matrices for classifiers with the best area under the receiver-operator curve (AUROC) among all patients, with the threshold set to match a 50% true positive rate
From: Validation of sleep-based actigraphy machine learning models for prediction of preterm birth
(a) Actigraphy/Gaussian naive Bayes | ||
---|---|---|
 | PN | PP |
TN | 0.605 | 0.258 |
TP | 0.076 | 0.061 |
(b) Case Reports/Gaussian naive Bayes | ||
---|---|---|
 | PN | PP |
TN | 0.701 | 0.162 |
TP | 0.076 | 0.061 |
(c) All/Gaussian naive Bayes | ||
---|---|---|
 | PN | PP |
TN | 0.734 | 0.129 |
TP | 0.095 | 0.043 |