PAPERS . NIPS . CC {
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Title:
LightGBM: A Highly Efficient Gradient Boosting Decision Tree
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Keywords {🔍}
information, data, gain, goss, features, gbdt, feature, instances, efb, lightgbm, gradient, boosting, decision, tree, algorithm, implementations, size, estimate, split, bundling, gradients, prove, exclusive, reduce, number, accuracy, neurips, proceedings, search, highly, efficient, part, advances, neural, processing, systems, nips, bibtex, metadata, paper, reviews, supplemental, authors, guolin, meng, thomas, finley, taifeng, wang, wei,
Topics {✒️}
emph{gradient-based emph{exclusive feature bundling} nonzero values simultaneously good approximation ratio larger gradients play split point determination efb \emph{lightgbm} smaller data size exclusive features optimal bundling greedy algorithm lightgbm speeds data size split points small gradients information gain feature dimension data instances qi meng thomas finley taifeng wang wei chen weidong ma qiwei ye engineering optimizations major reason time consuming side sampling} significant proportion important role accurate estimation np-hard training process change policy gbdt implementation conventional gbdt effective implementations effectively reduce feature features gbdt efb implementations reduce advances nips 2017 xgboost pgbrt adopted efficiency
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