Implementing local-explainability in Gradient Boosting Trees: Feature Contribution

arXiv:2402.09197v1 Announce Type: new
Abstract: 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 …

abstract artificial artificial intelligence arxiv boost boosting box cs.lg cs.lo decision decision trees ensemble explainability explainable artificial intelligence feature gradient information intelligence multiple nature tree trees type xai