Tag: XGBoost

Using SHAP, ALE, and LIME explainable AI techniques on daily data from 2008 to 2025, researchers show that oil market ...

Researchers used automated machine learning and cluster analysis of Nigeria Malaria Indicator Survey data to predict malaria risk in minors ...

Researchers fused Sentinel-5P, MODIS, and Sentinel-2 data with XGBoost machine learning to produce 250-meter PM2.5 maps for Lahore, revealing strong ...

An explainable generative deep learning framework called SYNERGY-VAE has uncovered three hidden population subgroups with distinct health signatures and depression ...

Researchers at ETH Zurich and Roche have combined active learning with geometric and self-supervised deep learning to predict C–H borylation ...

A machine learning study of 501 households in flood-affected Noakhali and Feni, Bangladesh, identifies the key predictors of waterborne disease ...

Researchers combined a genetic algorithm with machine learning price forecasting to cut 643 tonnes of steel from a 15 MW ...

Researchers in Türkiye used an explainable machine learning framework combining XGBoost and SHAP to identify age and postpartum diseases as ...

A high-resolution simulation of the South China Sea shows that the local dissipation ratio of internal tides varies from 0.3 ...

Vietnamese researchers combined field trials with machine learning to show that a precisely timed spray of the cytokinin 6-benzyladenine boosts ...