In this paper, we compare four state-of-the-art gradient boosting algorithms viz. XGBoost, CatBoost, LightGBM and SnapBoost. All these algorithms are a form of Gradient Boosting Decision Trees(GBDTs).

The goals of this Perspective are threefold: (1) to inform a broad audience, including machine learning (ML) and artificial intelligence (AI) academics and professionals, about synthetic drug ...

The strategic advantage of QML continues to expand its presence in industries that deal with complex, high-dimensional data.

Simplified flow cytometry-based assay for rapid multi-cytokine profiling and machine-learning-assisted diagnosis of inflammatory diseases ...

Traffic classification (TC) is a fundamental task of network management and monitoring operations. Previous works relying on selected packet header fields (e.g. port numbers) or application layer ...

Thus, the XGBoost model stands out from the comparison with different ML algorithms including transfer machine learning (25) and random forest (24) models due to its excellent performance, although ...

Machine learning could improve the timely identification of trauma patients in need of hemorrhage control resuscitation (HCR), but the real-life performance remains unknown. The ShockMatrix study ...