mlsanityData to 7 Oct 2026

Explanations of model predictions with live and breakDown packages

arXiv 1804.01955 ↗First posted 5 Apr 2018stat.ML cs.LG stat.APcited by 17

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Abstract (arXiv)

Complex models are commonly used in predictive modeling. In this paper we present R packages that can be used to explain predictions from complex black box models and attribute parts of these predictions to input features. We introduce two new approaches and corresponding packages for such attribution, namely live and breakDown. We also compare their results with existing implementations of state of the art solutions, namely lime that implements Locally Interpretable Model-agnostic Explanations and ShapleyR that implements Shapley values.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Apr 2018.

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Citing papers per year: 2021: 5; 2022: 2; 2023: 5; 2024: 4; 2025: 1.

17 papers.

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  1. 2021Synthetic Benchmarks for Scientific Research in Explainable Machine Learningcited by 59
  2. 2021Explainable Artificial Intelligence Approaches: A Surveycited by 40
  3. 2021Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley Valuescited by 24
  4. 2023Interpretable Mesomorphic Networks for Tabular Datacited by 6
  5. 2022Model Transparency and Interpretability : Survey and Application to the Insurance Industrycited by 5
  6. 2021SQAPlanner: Generating Data-Informed Software Quality Improvement Planscited by 4
  7. 2023Rationalizing Predictions by Adversarial Information Calibrationcited by 4
  8. 2023On the Relationship Between Interpretability and Explainability in Machine Learningcited by 3
  9. 2024Distilled Datamodel with Reverse Gradient Matchingcited by 3
  10. 2023Explainable AI for clinical risk prediction: a survey of concepts, methods, and modalitiescited by 2
  11. 2024A Systematic Literature Review on Explainability for Machine/Deep Learning-based Software Engineering Researchcited by 2
  12. 2021Interactive slice visualization for exploring machine learning modelscited by 1
5 more
  1. 2022Do We Need Another Explainable AI Method? Toward Unifying Post-hoc XAI Evaluation Methods into an Interactive and Multi-dimensional Benchmarkcited by 0
  2. 2023Uncertainty in Additive Feature Attribution methodscited by 0
  3. 2024Towards Explainable Test Case Prioritisation with Learning-to-Rank Modelscited by 0
  4. 2024nn2poly: An R Package for Converting Neural Networks into Interpretable Polynomialscited by 0
  5. 2025Your Model Is Unfair, Are You Even Aware? Inverse Relationship Between Comprehension and Trust in Explainability Visualizations of Biased ML Modelscited by 0