# Tweedie Gradient Boosting for Extremely Unbalanced Zero-inflated Data

arXiv: https://arxiv.org/abs/1811.10192 · HTML: https://mlsanity.com/paper/1811.10192/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2018.

## Built on by (most cited first, 10 of 10)

- [Uncertainty-Aware Probabilistic Graph Neural Networks for Road-Level Traffic Accident Prediction](https://mlsanity.com/paper/2309.05072/) (2023)
- [Enhanced Gradient Boosting for Zero-Inflated Insurance Claims and Comparative Analysis of CatBoost, XGBoost, and LightGBM](https://mlsanity.com/paper/2307.07771/) (2023)
- [Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time series](https://mlsanity.com/paper/2310.13029/) (2023)
- [Learning accurate and interpretable tree-based models](https://mlsanity.com/paper/2405.15911/) (2024)
- [Predicting band gap from chemical composition: A simple learned model for a material property with atypical statistics](https://mlsanity.com/paper/2501.02932/) (2025)
- [Identifying and Overcoming Transformation Bias in Forecasting Models](https://mlsanity.com/paper/2208.12264/) (2022)
- [Zero-Inflated Tweedie Boosted Trees with CatBoost for Insurance Loss Analytics](https://mlsanity.com/paper/2406.16206/) (2024)
- [Bayesian CART models for aggregate claim modeling](https://mlsanity.com/paper/2409.01908/) (2024)
- [From Noise to Precision: A Diffusion-Driven Approach to Zero-Inflated Precipitation Prediction](https://mlsanity.com/paper/2509.10501/) (2025)
- [One Size Fits None: Modeling NYC Taxi Trips](https://mlsanity.com/paper/2602.19404/) (2025)

## Abstract (arXiv)

Tweedie's compound Poisson model is a popular method to model insurance claims with probability mass at zero and nonnegative, highly right-skewed distribution. In particular, it is not uncommon to have extremely unbalanced data with excessively large proportion of zero claims, and even traditional Tweedie model may not be satisfactory for fitting the data. In this paper, we propose a boosting-assisted zero-inflated Tweedie model, called EMTboost, that allows zero probability mass to exceed a traditional model. We makes a nonparametric assumption on its Tweedie model component, that unlike a linear model, is able to capture nonlinearities, discontinuities, and complex higher order interactions among predictors. A specialized Expectation-Maximization algorithm is developed that integrates a blockwise coordinate descent strategy and a gradient tree-boosting algorithm to estimate key model parameters. We use extensive simulation and data analysis on synthetic zero-inflated auto-insurance claim data to illustrate our method's prediction performance.