# Unpack Local Model Interpretation for GBDT

arXiv: https://arxiv.org/abs/2004.01358 · HTML: https://mlsanity.com/paper/2004.01358/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2020.

## Built on by (most cited first, 1 of 1)

- [Comparative Analysis of Black-Box and White-Box Machine Learning Model in Phishing Detection](https://mlsanity.com/paper/2412.02084/) (2024)

## Abstract (arXiv)

A gradient boosting decision tree (GBDT), which aggregates a collection of single weak learners (i.e. decision trees), is widely used for data mining tasks. Because GBDT inherits the good performance from its ensemble essence, much attention has been drawn to the optimization of this model. With its popularization, an increasing need for model interpretation arises. Besides the commonly used feature importance as a global interpretation, feature contribution is a local measure that reveals the relationship between a specific instance and the related output. This work focuses on the local interpretation and proposes an unified computation mechanism to get the instance-level feature contributions for GBDT in any version. Practicality of this mechanism is validated by the listed experiments as well as applications in real industry scenarios.