Fig. 3From: Machine learning for the prediction of volume responsiveness in patients with oliguric acute kidney injury in critical careTraining process of the extreme gradient boosting machine. Sample output of bootstrap validation (BV) during XGBoost hyperparameter tuning, using the values specified in the final XGBoost model (learning rate = 0.04, minimum loss reduction = 10, maximum tree depth = 9, subsample = 0.6, and number of trees = 300). 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. The conditions of well-tuned model were satisfied: BV training log-loss decreases as the number of trees in an ensemble increases, and BV testing log-loss is less than 0.693 (e.g., a log-loss of 0.693 is the performance of a binary classifier that performs no better than chance: − log 0.5 ≈ 0.693) and only slightly more than BV training log-loss as the tree grows.Back to article page