  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.
