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AI Train·2014
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XGBoost

Tianqi Chen, Carlos Guestrin +1·University of Washington, Distributed Machine Learning Community (DMLC)

Trains gradient-boosted tree models across local and distributed environments, with bindings for Python, R, JVM, Julia, and C++. Its sparsity-aware split finding and quantile sketch made it a default baseline for tabular ML competitions.

#gradient-booting#ai-library#python#github#ai-train+1
AI Train·2017
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CatBoost

Yandex

Trains gradient-boosted decision trees with native categorical-feature handling, GPU acceleration, and production-ready prediction APIs. A strong fit for tabular ML when preprocessing categories into numeric features would add noise or leakage.

#gradient-booting#ai-library#python#github#ai-train+1
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