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

arXiv: https://arxiv.org/abs/1710.11555 · HTML: https://mlsanity.com/paper/1710.11555/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2017.

## Built on by (most cited first, 1 of 1)

- [Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library](https://mlsanity.com/paper/2212.02934/) (2022)

## Abstract (arXiv)

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.