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Accuracy evaluation and validation: AUC–ROC

JP Jun Hwan Park
JK Junggoo Kang
JK Jaemo Kang
DM Duhwan Mun
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The AUC–ROC was used to validate the results of the machine learning model. It is often used in machine learning33 and ground subsidence prediction11,13,34. The ROC curve uses the confusion matrix to check whether classification was performed correctly. Whether the actual classification result agrees with the inferred result is determined as shown in Fig. 533,35.

Confusion matrix.

The accuracy, sensitivity, and specificity can be calculated using Eqs. (12), (13), and (14), respectively, and the confusion matrix33.

The ROC curve refers to the sensitivity graph for 1-specificity, which starts from the origin (0, 0) and ends at (1, 1). For model validation, the AUC of the ROC curve was used. An AUC-ROC value of 1 indicates perfect classification, and a value of 0.5 indicates that classification was not realized35.

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