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 ...

The FPGA accelerated solution for the XGBoost algorithm is based on the Exact (Greedy) algorithm for tree creation. It can provide up to 26x speedup compared to a single threaded execution and up to ...