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Faster Boosting with Smaller Memory
Julaiti Alafate · Yoav S Freund

Wed Dec 11 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #7

State-of-the-art implementations of boosting, such as XGBoost and LightGBM, can process large training sets extremely fast. However, this performance requires that the memory size is sufficient to hold a 2-3 multiple of the training set size. This paper presents an alternative approach to implementing the boosted trees, which achieves a significant speedup over XGBoost and LightGBM, especially when the memory size is small. This is achieved using a combination of three techniques: early stopping, effective sample size, and stratified sampling. Our experiments demonstrate a 10-100 speedup over XGBoost when the training data is too large to fit in memory.

Author Information

Julaiti Alafate (University of California San Diego)
Yoav S Freund (University of California, San Diego)

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