Implementing local-explainability in Gradient Boosting Trees: Feature
  Contribution
Ángel Delgado-Panadero Beatriz Hernández-Lorca María Teresa García-Ordás José Alberto Benítez-Andrades
Gradient Boost Decision Trees (GBDT) is a powerful additive model based on
tree ensembles. Its nature makes GBDT a black-box model even though there are
multiple explainable artificial intelligence (XAI) models obtaining information
by reinterpreting the model globally and locally. Each tree of the ensemble is
a transparent model itself but the final outcome is the result of a sum of
these trees and it is not easy to clarify.
  In this paper, a feature contribution method for GBDT is developed. The
proposed method takes advantage of the GBDT architecture to calculate the
contribution of each feature using the residue of each node. This algorithm
allows to calculate the sequence of node decisions given a prediction.
  Theoretical proofs and multiple experiments have been carried out to
demonstrate the performance of our method which is not only a local
explicability model for the GBDT algorithm but also a unique option that
reflects GBDTs internal behavior. The proposal is aligned to the contribution
of characteristics having impact in some artificial intelligence problems such
as ethical analysis of Artificial Intelligence (AI) and comply with the new
European laws such as the General Data Protection Regulation (GDPR) about the
right to explain and nondiscrimination.