# Explanations Based on Item Response Theory (eXirt): A Model-Specific Method to Explain Tree-Ensemble Model in Trust Perspective

arXiv: https://arxiv.org/abs/2210.09933 · HTML: https://mlsanity.com/paper/2210.09933/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 60 references that are in the map.
- First posted on arXiv 18 Oct 2022.

## Builds on (most cited first, 12 of 12)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models](https://mlsanity.com/paper/1612.08468/) (2016)
- [One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency](https://mlsanity.com/paper/2001.09734/) (2020)
- [dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python](https://mlsanity.com/paper/2012.14406/) (2020)
- [Decoding machine learning benchmarks](https://mlsanity.com/paper/2007.14870/) (2020)
- [Does Dataset Complexity Matters for Model Explainers?](https://mlsanity.com/paper/2107.02661/) (2021)
- [Explanation-by-Example Based on Item Response Theory](https://mlsanity.com/paper/2210.01638/) (2022)
- [Prediction of Homicides in Urban Centers: A Machine Learning Approach](https://mlsanity.com/paper/2008.06979/) (2020)

## Built on by (most cited first, 4 of 4)

- [How Reliable and Stable are Explanations of XAI Methods?](https://mlsanity.com/paper/2407.03108/) (2024)
- [Standing on the shoulders of giants](https://mlsanity.com/paper/2409.03151/) (2024)
- [Enhancing Classifier Evaluation: A Fairer Benchmarking Strategy Based on Ability and Robustness](https://mlsanity.com/paper/2504.09759/) (2025)
- [Beyond Random Sampling: Instance Quality-Based Data Partitioning via Item Response Theory](https://mlsanity.com/paper/2508.10628/) (2025)

## Abstract (arXiv)

In recent years, XAI researchers have been formalizing proposals and developing new methods to explain black box models, with no general consensus in the community on which method to use to explain these models, with this choice being almost directly linked to the popularity of a specific method. Methods such as Ciu, Dalex, Eli5, Lofo, Shap and Skater emerged with the proposal to explain black box models through global rankings of feature relevance, which based on different methodologies, generate global explanations that indicate how the model's inputs explain its predictions. In this context, 41 datasets, 4 tree-ensemble algorithms (Light Gradient Boosting, CatBoost, Random Forest, and Gradient Boosting), and 6 XAI methods were used to support the launch of a new XAI method, called eXirt, based on Item Response Theory - IRT and aimed at tree-ensemble black box models that use tabular data referring to binary classification problems. In the first set of analyses, the 164 global feature relevance ranks of the eXirt were compared with 984 ranks of the other XAI methods present in the literature, seeking to highlight their similarities and differences. In a second analysis, exclusive explanations of the eXirt based on Explanation-by-example were presented that help in understanding the model trust. Thus, it was verified that eXirt is able to generate global explanations of tree-ensemble models and also local explanations of instances of models through IRT, showing how this consolidated theory can be used in machine learning in order to obtain explainable and reliable models.