Helpful examples of explainability for XGBoost models.
Explainability of XGBoost and machine learning models refers to the ability to understand, interpret, and communicate how a model makes its predictions, providing insights into the model’s decision-making process and ensuring transparency, trustworthiness, and compliance with ethical and regulatory standards.