# Gradient Boosting With Piece-Wise Linear Regression Trees

arXiv: https://arxiv.org/abs/1802.05640 · HTML: https://mlsanity.com/paper/1802.05640/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Feb 2018.

## Built on by (most cited first, 7 of 7)

- [Fast Linear Model Trees by PILOT](https://mlsanity.com/paper/2302.03931/) (2023)
- [When are Deep Networks really better than Decision Forests at small sample sizes, and how?](https://mlsanity.com/paper/2108.13637/) (2021)
- [A Survey of Link Prediction in Temporal Networks](https://mlsanity.com/paper/2502.21185/) (2025)
- [Using Machine Learning Techniques to Identify Key Risk Factors for Diabetes and Undiagnosed Diabetes](https://mlsanity.com/paper/2105.09379/) (2021)
- [Forecasting with Hyper-Trees](https://mlsanity.com/paper/2405.07836/) (2024)
- [Learning Multi-Layered GBDT Via Back Propagation](https://mlsanity.com/paper/2109.11863/) (2021)
- [ParamBoost: Gradient Boosted Piecewise Cubic Polynomials](https://mlsanity.com/paper/2604.18864/) (2026)

## Abstract (arXiv)

Gradient Boosted Decision Trees (GBDT) is a very successful ensemble learning algorithm widely used across a variety of applications. Recently, several variants of GBDT training algorithms and implementations have been designed and heavily optimized in some very popular open sourced toolkits including XGBoost, LightGBM and CatBoost. In this paper, we show that both the accuracy and efficiency of GBDT can be further enhanced by using more complex base learners. Specifically, we extend gradient boosting to use piecewise linear regression trees (PL Trees), instead of piecewise constant regression trees, as base learners. We show that PL Trees can accelerate convergence of GBDT and improve the accuracy. We also propose some optimization tricks to substantially reduce the training time of PL Trees, with little sacrifice of accuracy. Moreover, we propose several implementation techniques to speedup our algorithm on modern computer architectures with powerful Single Instruction Multiple Data (SIMD) parallelism. The experimental results show that GBDT with PL Trees can provide very competitive testing accuracy with comparable or less training time.