Unpack Local Model Interpretation for GBDT
It addresses the need for local model interpretation in GBDT, which is incremental as it builds on existing global feature importance methods.
The paper tackles the problem of interpreting gradient boosting decision trees (GBDT) by proposing a unified computation mechanism for instance-level feature contributions, validated through experiments and real-world industry applications.
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.