Machine learningMachine learning

Explainable Gradient Boosting

Explainable Gradient Boosting combines the predictive power of gradient boosting ensembles with structured interpretability tools — principally SHAP (SHapley Additive exPlanations) — to produce models that are both highly accurate and transparently auditable. Practitioners obtain global feature rankings and individual-level explanations alongside standard performance metrics.

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Sources

  1. Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2, 56–67. DOI: 10.1038/s42256-019-0138-9
  2. Molnar, C. (2022). Interpretable Machine Learning: A Guide for Making Black Box Models Explainable (2nd ed.). christophm.github.io/interpretable-ml-book/ link

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Referenced by

ScholarGateExplainable Gradient Boosting (Explainable Gradient Boosting (Gradient Boosting with Post-hoc and Intrinsic Interpretability)). Retrieved 2026-06-04 from https://scholargate.app/en/machine-learning/explainable-gradient-boosting