# DALEX: explainers for complex predictive models

arXiv: https://arxiv.org/abs/1806.08915 · HTML: https://mlsanity.com/paper/1806.08915/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2018.

## Built on by (most cited first, 28 of 28)

- [To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods](https://mlsanity.com/paper/2106.00461/) (2021)
- [SurvSHAP(t): Time-dependent explanations of machine learning survival models](https://mlsanity.com/paper/2208.11080/) (2022)
- [Fooling Partial Dependence via Data Poisoning](https://mlsanity.com/paper/2105.12837/) (2021)
- [StyloMetrix: An Open-Source Multilingual Tool for Representing Stylometric Vectors](https://mlsanity.com/paper/2309.12810/) (2023)
- [Establishing and Evaluating Trustworthy AI: Overview and Research Challenges](https://mlsanity.com/paper/2411.09973/) (2024)
- [XAI for Cybersecurity: State of the Art, Challenges, Open Issues and Future Directions](https://mlsanity.com/paper/2206.03585/) (2022)
- [fairmodels: A Flexible Tool For Bias Detection, Visualization, And Mitigation](https://mlsanity.com/paper/2104.00507/) (2021)
- [Performance is not enough: the story told by a Rashomon quartet](https://mlsanity.com/paper/2302.13356/) (2023)
- [Integrating Reward Maximization and Population Estimation: Sequential Decision-Making for Internal Revenue Service Audit Selection](https://mlsanity.com/paper/2204.11910/) (2022)
- [Explainable expected goal models for performance analysis in football analytics](https://mlsanity.com/paper/2206.07212/) (2022)
- [shapr: Explaining Machine Learning Models with Conditional Shapley Values in R and Python](https://mlsanity.com/paper/2504.01842/) (2025)
- [Does Dataset Complexity Matters for Model Explainers?](https://mlsanity.com/paper/2107.02661/) (2021)
- [Glocal Explanations of Expected Goal Models in Soccer](https://mlsanity.com/paper/2308.15559/) (2023)
- [Enabling Machine Learning Algorithms for Credit Scoring -- Explainable Artificial Intelligence (XAI) methods for clear understanding complex predictive models](https://mlsanity.com/paper/2104.06735/) (2021)
- [Transforming Feature Space to Interpret Machine Learning Models](https://mlsanity.com/paper/2104.04295/) (2021)
- [Exploring Local Explanations of Nonlinear Models Using Animated Linear Projections](https://mlsanity.com/paper/2205.05359/) (2022)
- [counterfactuals: An R Package for Counterfactual Explanation Methods](https://mlsanity.com/paper/2304.06569/) (2023)
- [Triplot: model agnostic measures and visualisations for variable importance in predictive models that take into account the hierarchical correlation structure](https://mlsanity.com/paper/2104.03403/) (2021)
- [TrustyAI Explainability Toolkit](https://mlsanity.com/paper/2104.12717/) (2021)
- [survex: an R package for explaining machine learning survival models](https://mlsanity.com/paper/2308.16113/) (2023)
- [forester: A Tree-Based AutoML Tool in R](https://mlsanity.com/paper/2409.04789/) (2024)
- [Black Box Model Explanations and the Human Interpretability Expectations -- An Analysis in the Context of Homicide Prediction](https://mlsanity.com/paper/2210.10849/) (2022)
- [ONNXExplainer: an ONNX Based Generic Framework to Explain Neural Networks Using Shapley Values](https://mlsanity.com/paper/2309.16916/) (2023)
- [mlr3summary: Concise and interpretable summaries for machine learning models](https://mlsanity.com/paper/2404.16899/) (2024)
- [nn2poly: An R Package for Converting Neural Networks into Interpretable Polynomials](https://mlsanity.com/paper/2406.01588/) (2024)
- [neuralGAM: An R Package for Fitting Generalized Additive Neural Networks](https://mlsanity.com/paper/2505.08610/) (2025)
- [midr: Learning from Black-Box Models by Maximum Interpretation Decomposition](https://mlsanity.com/paper/2506.08338/) (2025)
- [Your Model Is Unfair, Are You Even Aware? Inverse Relationship Between Comprehension and Trust in Explainability Visualizations of Biased ML Models](https://mlsanity.com/paper/2508.00140/) (2025)

## Abstract (arXiv)

Predictive modeling is invaded by elastic, yet complex methods such as neural networks or ensembles (model stacking, boosting or bagging). Such methods are usually described by a large number of parameters or hyper parameters - a price that one needs to pay for elasticity. The very number of parameters makes models hard to understand. This paper describes a consistent collection of explainers for predictive models, a.k.a. black boxes. Each explainer is a technique for exploration of a black box model. Presented approaches are model-agnostic, what means that they extract useful information from any predictive method despite its internal structure. Each explainer is linked with a specific aspect of a model. Some are useful in decomposing predictions, some serve better in understanding performance, while others are useful in understanding importance and conditional responses of a particular variable. Every explainer presented in this paper works for a single model or for a collection of models. In the latter case, models can be compared against each other. Such comparison helps to find strengths and weaknesses of different approaches and gives additional possibilities for model validation. Presented explainers are implemented in the DALEX package for R. They are based on a uniform standardized grammar of model exploration which may be easily extended. The current implementation supports the most popular frameworks for classification and regression.