Results 61 to 70 of about 72,345 (262)
Previsión del consumo eléctrico en el cantón Salcedo mediante técnicas de aprendizaje automático
Revista OdigosEn respuesta al crecimiento de la demanda de energía eléctrica, este estudio se centra en la eficiente previsión del consumo eléctrico en el cantón Salcedo, Ecuador.
Oscar Fabricio Chicaiza Yugcha +3 more
doaj +1 more source
Hyperparameter search settings of the XGBoost models.
, 2023 Hyperparameter search settings of the XGBoost models.
Michiel E. Adriaens (10510526) +11 more
core +1 more source
Advanced Science, EarlyView.A machine learning‐assisted framework optimizes the KCl‐CaCl2‐LiCl ternary electrolyte. The optimized 13:35:52 mol% composition enables Ca‐based liquid metal batteries to operate stably at 480 °C, with >99.5% coulombic efficiency, ultralow self‐discharge, and excellent cycling stability, advancing low‐temperature large‐scale energy storage.
Xinglin Zhou +3 more
wiley +1 more source
Data-Driven Optimised XGBoost for Predicting the Performance of Axial Load Bearing Capacity of Fully Cementitious Grouted Rock Bolting Systems [PDF]
This article investigates the application of eXtreme gradient boosting (XGBoost) and hybrid metaheuristics optimisation techniques to predict the axial load bearing capacity of fully grouted rock bolting systems. For this purpose, a comprehensive dataset
Shahab Hosseini +15 more
core +1 more source
, 2023 This research aims to predict life expectancy in several Asian countries using the XGBoost Regressor algorithm. The data used is sourced from the UCI Machine Learning Repository. In this study, the researchers construct a predictive model using a machine
Kurniawan, Wildan, Indahyanti, Uce
core +1 more source
Advanced Science, EarlyView.Based on the largest printable mesoscopic perovskite solar cells database we established, stacking model achieved precise PCE prediction (R2 = 0.73, MAE = 2.18%). Multiple experiments verified the accuracy of the model, which guided the fabrication of high‐PCE devices with an efficiency of 19.36%.
Hao Meng +9 more
wiley +1 more source
Tree Boosting With XGBoost - Why Does XGBoost Win "Every" Machine Learning Competition? [PDF]
, 2016 Tree boosting has empirically proven to be a highly effective approach to predictive modeling. It has shown remarkable results for a vast array of problems. For many years, MART has been the tree boosting method of choice.
Nielsen, Didrik
core +1 more source
DMGutierrezz/XGB_globaldef_2023: XGBoost global deforestation analysis
<p>This code allows you to run a series of XGBoost models in R with random parameter settings using a 10-fold adaptive resampling procedure with Root Mean Square Error (RMSE) as the evaluation criterion.</p ...
DMGutierrezz
core +1 more source
A Comparative Analysis of XGBoost
, 2019 XGBoost is a scalable ensemble technique based on gradient boosting that has demonstrated to be a reliable and efficient machine learning challenge solver.
Martínez Muñoz, Gonzalo +3 more
core +1 more source
Machine‐Learning Framework for Designing Stable Interfaces in All‐Solid‐State Lithium‐Ion Batteries
Advanced Science, EarlyView.A data‐driven strategy is developed to discover coating materials for all‐solid‐state lithium batteries. Using calculations of interfacial reactivity, unsupervised pattern recognition, and machine‐learning prediction, the study identifies low‐reactivity compositional patterns and screens new lithium‐based oxide and polyanion candidates, extending ...
Sehyeok Park +4 more
wiley +1 more source