Table 4 Comparison of the performance of XGBoost and RF models by confusion matrix and evaluation measures.

From: The construction of HMME-PDT efficacy prediction model for port-wine stain based on machine learning algorithms

Models

Testing dataset

Predict ineffective

Predict effective

Accuracy

Precision

Recall

F1-score

AUC

Extreme Gradient Boosting (XGBoost)

ineffective

4

1

0.8750

0.8750

0.8750

0.8750

0.8636

effective

1

10

Random Forest (RF)

ineffective

4

1

0.8125

0.8271

0.8125

0.8166

0.8818

effective

2

9

  1. AUC the area under the curve.
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