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arXiv · 1710.11555

TF Boosted Trees: A scalable TensorFlow based framework for gradient boosting

Natalia Ponomareva·Soroush Radpour·Gilbert Hendry·Salem Haykal·Thomas Colthurst·Petr Mitrichev·Alexander Grushetsky
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Abstract

TF Boosted Trees (TFBT) is a new open-sourced frame-work for the distributed training of gradient boosted trees. It is based on TensorFlow, and its distinguishing features include a novel architecture, automatic loss differentiation, layer-by-layer boosting that results in smaller ensembles and faster prediction, principled multi-class handling, and a number of regularization techniques to prevent overfitting.

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Natalia Ponomareva, Soroush Radpour, Gilbert Hendry, Salem Haykal, Thomas Colthurst, Petr Mitrichev, Alexander Grushetsky. 2017-10-31. TF Boosted Trees: A scalable TensorFlow based framework for gradient boosting. https://arxiv.org/abs/1710.11555

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