This article provides a non-technical guide to interpreting SHAP analyses, useful for explaining machine learning models to non-technical stakeholders, with a focus on both local and global interpretability using various visualization methods.
This article explores the use of Isolation Forest for anomaly detection and how SHAP (KernelSHAP and TreeSHAP) can be applied to explain the anomalies detected, providing insights into which features contribute to anomaly scores.
