# Grabit: Gradient Tree-Boosted Tobit Models for Default Prediction

arXiv: https://arxiv.org/abs/1711.08695 · HTML: https://mlsanity.com/paper/1711.08695/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2017.

## Built on by (most cited first, 6 of 6)

- [The AI Revolution: Opportunities and Challenges for the Finance Sector](https://mlsanity.com/paper/2308.16538/) (2023)
- [NURD: Negative-Unlabeled Learning for Online Datacenter Straggler Prediction](https://mlsanity.com/paper/2203.08339/) (2022)
- [Interpretable Selective Learning in Credit Risk](https://mlsanity.com/paper/2209.10127/) (2022)
- [A Spatio-Temporal Machine Learning Model for Mortgage Credit Risk: Default Probabilities and Loan Portfolios](https://mlsanity.com/paper/2410.02846/) (2024)
- [CatBoost model with synthetic features in application to loan risk assessment of small businesses](https://mlsanity.com/paper/2106.07954/) (2021)
- [Look Who's Talking: Interpretable Machine Learning for Assessing Italian SMEs Credit Default](https://mlsanity.com/paper/2108.13914/) (2021)

## Abstract (arXiv)

A frequent problem in binary classification is class imbalance between a minority and a majority class such as defaults and non-defaults in default prediction. In this article, we introduce a novel binary classification model, the Grabit model, which is obtained by applying gradient tree boosting to the Tobit model. We show how this model can leverage auxiliary data to obtain increased predictive accuracy for imbalanced data. We apply the Grabit model to predicting defaults on loans made to Swiss small and medium-sized enterprises (SME) and obtain a large and significant improvement in predictive performance compared to other state-of-the-art approaches.