Faster Boosting with Smaller Memory
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The two state-of-the-art implementations of boosted trees: XGBoost and LightGBM, can process large training sets extremely fast. However, this performance requires that memory size is sufficient to hold a 2-3 multiple of the training set size. This paper presents an alternative approach to implementing boosted trees. which achieves a significant speedup over XGBoost and LightGBM, especially when memory size is small. This is achieved using a combination of two techniques: early stopping and stratified sampling, which are explained and analyzed in the paper. We describe our implementation and present experimental results to support our claims.
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