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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.
This article explains the concept and use of Friedman's H-statistic for finding interactions in machine learning models.
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