TF Boosted Trees (TFBT) is a new open-sourced framework 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.
CITATION STYLE
Ponomareva, N., Radpour, S., Hendry, G., Haykal, S., Colthurst, T., Mitrichev, P., & Grushetsky, A. (2017). TF Boosted Trees: A Scalable TensorFlow Based Framework for Gradient Boosting. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10536 LNAI, pp. 423–427). Springer Verlag. https://doi.org/10.1007/978-3-319-71273-4_44
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