# Why Are You Weird? Infusing Interpretability in Isolation Forest for Anomaly Detection

arXiv: https://arxiv.org/abs/2112.06858 · HTML: https://mlsanity.com/paper/2112.06858/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 15 references that are in the map.
- First posted on arXiv 13 Dec 2021.

## Builds on (most cited first, 5 of 5)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [On the Robustness of Interpretability Methods](https://mlsanity.com/paper/1806.08049/) (2018)
- [Model-Agnostic Interpretability of Machine Learning](https://mlsanity.com/paper/1606.05386/) (2016)
- [Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature Importance](https://mlsanity.com/paper/2007.11117/) (2020)

## Built on by (most cited first, 5 of 5)

- [A Survey on Explainable Anomaly Detection](https://mlsanity.com/paper/2210.06959/) (2022)
- [AcME-AD: Accelerated Model Explanations for Anomaly Detection](https://mlsanity.com/paper/2403.01245/) (2024)
- [FoMo X: Modular Explainability Signals for Outlier Detection Foundation Models](https://mlsanity.com/paper/2603.17570/) (2026)
- [Extending Decision Predicate Graphs for Comprehensive Explanation of Isolation Forest](https://mlsanity.com/paper/2505.04019/) (2025)
- [Explanation Method for Anomaly Detection on Mixed Numerical and Categorical Spaces](https://mlsanity.com/paper/2209.04173/) (2022)

## Abstract (arXiv)

Anomaly detection is concerned with identifying examples in a dataset that do not conform to the expected behaviour. While a vast amount of anomaly detection algorithms exist, little attention has been paid to explaining why these algorithms flag certain examples as anomalies. However, such an explanation could be extremely useful to anyone interpreting the algorithms' output. This paper develops a method to explain the anomaly predictions of the state-of-the-art Isolation Forest anomaly detection algorithm. The method outputs an explanation vector that captures how important each attribute of an example is to identifying it as anomalous. A thorough experimental evaluation on both synthetic and real-world datasets shows that our method is more accurate and more efficient than most contemporary state-of-the-art explainability methods.