WQI Improvement Based on XG-BOOST Algorithm and Exploration of Optimal Indicator SetWQIgroundwaterXG-BOOSToptimal indicator set screeningThis paper takes a portion of the Manas River Basin in Xinjiang Province, China, as an example and proposes an improved traditional comprehensive water quality index ...
eXtreme Gradient boost (XGboost) is one of the most reliable machine learning classifiers, and has been widely applied to bioinformatics problems28,29. It is based on a tree model that utilizes a boosting algorithm for classification. To reduce the complexity of the model and control overfitting,...
Then we use the machine learning algorithm XG-Boost to predict the churn of customers before and after the subdivision. The research found that the prediction accuracy is higher after customer segmentation. In addition, the XG-Boost algorithm is more advantageous than other algorithms....
Using XG Boost, the decision-tree-based ensemble Machine Learning algorithm that uses a gradient-boosting framework and a simultaneous formula was developed to predict both fragmentation and ground vibration using joint angle and the same set of parameters.Chandrahas, N. Sri...
Time-consuming optimization methods to predict the S-parameters in customized Electromagnetic (EM) simulators such as HFSS (High Frequency Structure Simulator) have been resolved using the proposed XG-Boost regression approach. In this paper, the proposed XG-Boost approach minimizes the switch design ...
Learn more OK, Got it. Something went wrong and this page crashed! If the issue persists, it's likely a problem on our side. Unexpected end of JSON inputkeyboard_arrow_upcontent_copySyntaxError: Unexpected end of JSON inputRefresh