Extending Decision Predicate Graphs for Comprehensive Explanation of Isolation Forest

TL;DR

This paper introduces a novel explainability method for Isolation Forest, using Decision Predicate Graphs and IOP-Score to clarify outlier detection logic and decision boundaries, enhancing transparency in anomaly detection models.

Contribution

The paper presents a new global explainability approach for Isolation Forest based on Decision Predicate Graphs and IOP-Score, improving interpretability of outlier detection.

Findings

Enhanced explainability of Isolation Forest models.

Provides insights into feature contributions and decision boundaries.

Advances state-of-the-art in model transparency for outlier detection.

Abstract

The need to explain predictive models is well-established in modern machine learning. However, beyond model interpretability, understanding pre-processing methods is equally essential. Understanding how data modifications impact model performance improvements and potential biases and promoting a reliable pipeline is mandatory for developing robust machine learning solutions. Isolation Forest (iForest) is a widely used technique for outlier detection that performs well. Its effectiveness increases with the number of tree-based learners. However, this also complicates the explanation of outlier selection and the decision boundaries for inliers. This research introduces a novel Explainable AI (XAI) method, tackling the problem of global explainability. In detail, it aims to offer a global explanation for outlier detection to address its opaque nature. Our approach is based on the Decision Predicate Graph (DPG), which clarifies the logic of ensemble methods and provides both insights and a graph-based metric to explain how samples are identified as outliers using the proposed Inlier-Outlier Propagation Score (IOP-Score). Our proposal enhances iForest's explainability and provides a comprehensive view of the decision-making process, detailing which features contribute to outlier identification and how the model utilizes them. This method advances the state-of-the-art by providing insights into decision boundaries and a comprehensive view of holistic feature usage in outlier identification. -- thus promoting a fully explainable machine learning pipeline.