https://github.com/statmixedml/dgbm

Distributional Gradient Boosting Machines
https://github.com/statmixedml/dgbm

decision-trees distributional-regression gradient-boosting lightgbm machine-learning normalizing-flow probabilistic-modeling quantile-estimation uncertainty-quantification xgboost

Last synced: about 2 months ago
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Distributional Gradient Boosting Machines

• Host: GitHub
• URL: https://github.com/statmixedml/dgbm
• Owner: StatMixedML
• License: apache-2.0
• Created: 2022-04-02T06:14:23.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2022-12-13T12:12:15.000Z (over 2 years ago)
• Last Synced: 2025-01-30T17:39:31.955Z (4 months ago)
• Topics: decision-trees, distributional-regression, gradient-boosting, lightgbm, machine-learning, normalizing-flow, probabilistic-modeling, quantile-estimation, uncertainty-quantification, xgboost
• Homepage:
• Size: 169 KB
• Stars: 27
• Watchers: 7
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# DGBM: Distributional Gradient Boosting Machines

We present a unified probabilistic gradient boosting framework for regression tasks that models and predicts the entire conditional distribution of a univariate response variable as a function of covariates. Our likelihood-based approach allows us to either model all conditional moments of a parametric distribution, or to approximate the conditional cumulative distribution function via Normalizing Flows. As underlying computational backbones, our framework is based on XGBoost and LightGBM. Modelling and predicting the entire conditional distribution greatly enhances existing tree-based gradient boosting implementations, as it allows to create probabilistic forecasts from which prediction intervals and quantiles of interest can be derived.

## News

:construction: Repo is under construction

## Reference Paper

[![Arxiv link](https://img.shields.io/badge/arXiv-Distributional%20Gradient%20Boosting%20Machines-color=brightgreen)](https://arxiv.org/abs/2204.00778)