XGBoost
XGBoost on World Data Ocean: a running collection of 3 stories we have gathered and hand-picked because they are worth your time. Every post here touches on xgboost in some way — the news, the analysis, the deep dives, and the occasional surprise find. A Global Hub for Ocean Intelligence 4 World Data Ocean is a centralized digital platform where researchers, scientists, and ocean enthusiasts converge to explore, analyze, and… New stories are added to this page as we find them, so check back if you want to keep up with what is happening around xgboost, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything World Data Ocean is covering right now.
A resilient forecasting approach of China’s export container freight index under geopolitical shocks based on a multi-factor screening and multi-model comparison
Geopolitical shocks introduce significant nonlinearity and non-stationarity to China’s Containerized Freight Index (CCFI), challenging traditional forecasting accuracy. This study rigorously evaluates model resilience, comparing ARIMA, Random Forest, XGBoost, and LSTM approaches under both stable and volatile market conditions. Utilizing a multi-factor screening process, eight core influencing factors were identified, and results demonstrate LSTM's superior performance during periods of extreme volatility, notably outperforming ARIMA.
Coupling coordination between integrated transport–shipping system and nearshore marine ecology in China’s coastal cities: spatiotemporal evolution and XGBoost–SHAP-based mechanism identification
Achieving a dynamic equilibrium between integrated transport-shipping systems (ITS) and nearshore marine ecology (NME) is paramount for the sustainable development of China’s coastal cities. This study, utilizing longitudinal panel data from 53 cities (2013-2024), assesses the coupling coordination degree (CCD) between these systems. Employing the XGBoost–SHAP approach, we identify key influencing mechanisms, revealing that urbanization and trade dependence significantly shape CCD outcomes. For further exploration of related data-driven approaches, see our article, "Machine learning predictions for microbial eukaryotic plankton."
Machine learning predictions for microbial eukaryotic plankton: implications from unevenly structured data
Machine learning offers a promising avenue for predicting eukaryotic microbial plankton diversity from environmental data; however, model generalizability remains a critical challenge. This study utilized XGBoost to model 18S rRNA gene Shannon Diversity Index (SDI) across the Mediterranean Sea, revealing significant limitations in transferability due to unevenly structured data. Performance declined substantially when tested against independent datasets, highlighting the need for spatially explicit evaluation and standardized protocols. Understanding these constraints is essential for robust ocean intelligence.