# GBDT-MO: Gradient Boosted Decision Trees for Multiple Outputs

arXiv: https://arxiv.org/abs/1909.04373 · HTML: https://mlsanity.com/paper/1909.04373/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2019.

## Built on by (most cited first, 16 of 16)

- [NCART: Neural Classification and Regression Tree for Tabular Data](https://mlsanity.com/paper/2307.12198/) (2023)
- [SketchBoost: Fast Gradient Boosted Decision Tree for Multioutput Problems](https://mlsanity.com/paper/2211.12858/) (2022)
- [Multi-Target XGBoostLSS Regression](https://mlsanity.com/paper/2210.06831/) (2022)
- [Wasserstein Gradient Boosting: A Framework for Distribution-Valued Supervised Learning](https://mlsanity.com/paper/2405.09536/) (2024)
- [SecureBoost+: Large Scale and High-Performance Vertical Federated Gradient Boosting Decision Tree](https://mlsanity.com/paper/2110.10927/) (2021)
- [Tree-Based Dynamic Classifier Chains](https://mlsanity.com/paper/2112.06672/) (2021)
- [Condensed Gradient Boosting](https://mlsanity.com/paper/2211.14599/) (2022)
- [Improving the Timing Resolution of Positron Emission Tomography Detectors Using Boosted Learning -- A Residual Physics Approach](https://mlsanity.com/paper/2302.01681/) (2023)
- [Multi-Task Learning for Post-transplant Cause of Death Analysis: A Case Study on Liver Transplant](https://mlsanity.com/paper/2304.00012/) (2023)
- [Gradient-based Label Binning in Multi-label Classification](https://mlsanity.com/paper/2106.11690/) (2021)
- [Robust-Multi-Task Gradient Boosting](https://mlsanity.com/paper/2507.11411/) (2025)
- [Learning Multi-Layered GBDT Via Back Propagation](https://mlsanity.com/paper/2109.11863/) (2021)
- [When a CBR in Hand is Better than Twins in the Bush](https://mlsanity.com/paper/2305.05111/) (2023)
- [Robust-GBDT: GBDT with Nonconvex Loss for Tabular Classification in the Presence of Label Noise and Class Imbalance](https://mlsanity.com/paper/2310.05067/) (2023)
- [Effective Predictive Modeling for Emergency Department Visits and Evaluating Exogenous Variables Impact: Using Explainable Meta-learning Gradient Boosting](https://mlsanity.com/paper/2411.11275/) (2024)
- [A Review and Analysis of a Parallel Approach for Decision Tree Learning from Large Data Streams](https://mlsanity.com/paper/2505.11780/) (2025)

## Abstract (arXiv)

Gradient boosted decision trees (GBDTs) are widely used in machine learning, and the output of current GBDT implementations is a single variable. When there are multiple outputs, GBDT constructs multiple trees corresponding to the output variables. The correlations between variables are ignored by such a strategy causing redundancy of the learned tree structures. In this paper, we propose a general method to learn GBDT for multiple outputs, called GBDT-MO. Each leaf of GBDT-MO constructs predictions of all variables or a subset of automatically selected variables. This is achieved by considering the summation of objective gains over all output variables. Moreover, we extend histogram approximation into multiple output case to speed up the training process. Various experiments on synthetic and real-world datasets verify that GBDT-MO achieves outstanding performance in terms of both accuracy and training speed. Our codes are available on-line.