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. 🚀 SHAP Course 🚀 SHAP course: https://adataodyssey.com/courses/shap-with-python/ The first 20 people to use the coupon code "IFSHAP24" will get 100% off! 🚀 Free XAI Courses 🚀 Signup here: https://mailchi.mp/40909011987b/signup XAI course: https://adataodyssey.com/courses/xai-with-python/ 🚀 Companion article with link to code (no-paywall link): 🚀 https://medium.com/data-science/explaining-anomalies-with-isolation-forest-and-shap-0d5d1224b918?sk=38c81f496f2cd5fd31591aecd0bd2adf 🚀 Learn about Isolation Forests 🚀 https://www.datacamp.com/tutorial/isolation-forest 🚀 Useful playlists 🚀 XAI: https://www.youtube.com/playlist?list=PLqDyyww9y-1SwNZ-6CmvfXDAOdLS7yUQ4 SHAP: https://www.youtube.com/playlist?list=PLqDyyww9y-1SJgMw92x90qPYpHgahDLIK Algorithm fairness: https://www.youtube.com/playlist?list=PLqDyyww9y-1Q0zWbng6vUOG1p3oReE2xS 🚀 Get in touch 🚀 Medium: https://conorosullyds.medium.com/ Threads: https://www.threads.net/@conorosullyds Twitter: https://twitter.com/conorosullyDS Website: https://adataodyssey.com/ 🚀 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