Explaining Anomalies with Isolation Forest and SHAP | Python Tutorial

In this video, we dive deep into the world of anomaly detection with a focus on the Isolation Forest algorithm. Isolation Forest is a powerful machine learning model for identifying outliers in high-dimensional data, but understanding why an anomaly is detected can be a challenge. That's where SHAP (SHapley Additive exPlanations) comes in.

We'll explore how to use both KernelSHAP and TreeSHAP to interpret the contributions of individual features to anomaly scores. You'll learn how to visualize and break down these contributions, making it easier to understand and explain the decisions made by Isolation Forest. This is particularly valuable in real-world applications like fraud detection, where knowing the 'why' behind an anomaly is just as important as identifying it.


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SHAP course: https://adataodyssey.com/courses/shap...
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🚀 Companion article with link to code (no-paywall link): 🚀
[https://medium.com/towards-data-scien...

🚀 Learn about Isolation Forests 🚀
https://www.datacamp.com/tutorial/iso...

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XAI:    • Explainable AI (XAI)  
SHAP:    • SHAP  
Algorithm fairness:    • Algorithm Fairness  


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🚀 Sections 🚀
00:00 Introduction
01:35 What is Anomaly Detection?
02:28 What is Isolation Forest?
05:57 Interpreting SHAP Values for Isolation Forest
07:44 Model Training
15:28 KernelSHAP with Anomaly Score
21:17 TreeSHAP with Average Path Length