Tag: XGBoost

A study using the Karun-I dam as a case study finds that Random Forest-based machine learning methods offer superior prediction ...

A 2026 study in Carbon Balance and Management uses machine learning to model carbon density in Northwest China, identifying climatic ...

Researchers evaluated a hybrid negative sampling strategy combining frequency ratio pre-screening and positive–unlabeled learning for landslide susceptibility modeling in a ...

A comparative analysis in Natural Resources Research shows that five machine learning algorithms achieve over 90% accuracy in lithology prediction ...

Researchers evaluated eight machine learning models on high-performance concrete mixtures, demonstrating that optimized gradient boosted algorithms and interpretability frameworks accurately ...

A two-country naturalistic driving study evaluated conditional generative adversarial networks for classifying rare, dangerous driving behaviors alongside explainable machine learning ...

A UK Biobank study of 457,682 adults found atrial fibrillation more than doubled the risk of chronic kidney disease, and ...

A study published in BMC Artificial Intelligence evaluates machine learning models for predicting colorectal cancer outcomes in Palestine and globally, ...

A grid-scale machine learning investigation of Shanghai reveals how urban spatial morphology, economic activities, and natural resource values interact across ...

Researchers demonstrated that pairing a custom convolutional neural network with gradient boosted decision trees significantly improves the classification of damaged ...