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).

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 ...

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 ...

March 17, 2023 — Using Intel hardware, Intel Integrated Performance Primitives (Intel IPP) and Intel oneAPI Data Analytics Library (oneDAL), Anodot improved the performance of its autocorrelation ...

They used logistic regression, decision tree, XGBoost, and random forest algorithms. These are supervised binary classification algorithms.