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Fig. 2 | BMC Medical Informatics and Decision Making

Fig. 2

From: Machine learning model for predicting acute kidney injury progression in critically ill patients

Fig. 2

The training process of the extreme gradient boosting machine. A Cross-validation during XGBoost hyperparameter tuning. The log-loss value for the training and testing datasets is shown in the vertical axis. The dashed vertical line indicates the number of rounds with the minimum log-loss in the test sample. B Learning curve of the XGBoost model after hyperparameter tuning. AU-ROC value for the testing and training datasets is shown in the vertical axis. With the subsample ratio increasing, AU-ROC of training datasets decreases, and AU-ROC of testing datasets increases. The training score is always higher than the test score

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