Author:
Tymoteusz Miller,Kozlovska Polina,Łobodzińska Adrianna,Lewita Klaudia,Żejmo Julia,Kaczanowska Oliwia
Abstract
The recent release of XGBoost 2.0, an advanced machine learning library, embodies a substantial advancement in analytical tools available for climate science research. With its novel features like Multi-Target Trees with Vector-Leaf Outputs, enhanced scalability, and computational efficiency improvements, XGBoost 2.0 is poised to significantly aid climate scientists in dissecting complex climate data, thereby fostering a deeper understanding of climate dynamics. This article delves into the key features of XGBoost 2.0 and elucidates its potential applications and benefits in the domain of climate science analytics.
Publisher
European Scientific Platform (Publications)
Subject
General Agricultural and Biological Sciences