XGBoost had a higher accuracy (area under the receiver operator characteristic curve [AUC] = 0.747) than the 2 other classifiers, logistic regression (AUC = 0.701) and random forest (AUC = 0.719).

Dutch scientists have developed a PV forecasting method that uses the XGBoost algorithm. They claim their approach predicts electricity generation levels an hour ahead for big fleets of ...

Using the XGBoost algorithm, we developed a classifier incorporating nine genes (ARHGAP9, CADM1, CPE, DUSP3, FGFR1, GALNT3, IGF2BP3, KIF26A, ZFP3). In our internal cohort, the classifier exhibited ...

Four model testing results revealed the XGBoost algorithm performing best (AUROC = 0.895). A tuned version of this model was then used for model prediction validation under the name ‘AutMedAI.’ ...