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

Unpack Local Model Interpretation for GBDT