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From SHAP to EBM: Explain your Gradient Boosting Models in Python

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Evento:
AI Conf 2025
Lingua:
Italiano

Tag

  • Artificial Intelligence
  • Machine Learning
  • Python

Speaker

From SHAP to EBM: Explain your Gradient Boosting Models in Python

XGBoost excels in machine learning on tabular data, but its tree-based models are hard to interpret. This talk introduces two leading interpretability techniques: SHAP (SHapley Additive exPlanations) and EBM (Explainable Boosting Machine). We'll explore their theoretical foundations, compare their strengths and limitations, and demonstrate their implementation using Python's shap and interpret-ml packages. Attendees will learn how these methods work and how to apply them to explain gradient boosting models.