# Surrogate Locally-Interpretable Models with Supervised Machine Learning Algorithms

arXiv: https://arxiv.org/abs/2007.14528 · HTML: https://mlsanity.com/paper/2007.14528/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2020.

## Built on by (most cited first, 10 of 10)

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- [Leveraging Model-based Trees as Interpretable Surrogate Models for Model Distillation](https://mlsanity.com/paper/2310.03112/) (2023)
- [Surrogate Modeling and Explainable Artificial Intelligence for Complex Systems: A Workflow for Automated Simulation Exploration](https://mlsanity.com/paper/2510.16742/) (2025)
- [Interpretable Machine Learning based on Functional ANOVA Framework: Algorithms and Comparisons](https://mlsanity.com/paper/2305.15670/) (2023)
- [Using Model-Based Trees with Boosting to Fit Low-Order Functional ANOVA Models](https://mlsanity.com/paper/2207.06950/) (2022)
- [Monotone Tree-Based GAMI Models by Adapting XGBoost](https://mlsanity.com/paper/2309.02426/) (2023)
- [Effector: A Python package for regional explanations](https://mlsanity.com/paper/2404.02629/) (2024)
- [Shapley Computations Using Surrogate Model-Based Trees](https://mlsanity.com/paper/2207.05214/) (2022)
- [Interpreting Inflammation Prediction Model via Tag-based Cohort Explanation](https://mlsanity.com/paper/2410.14082/) (2024)
- [Interpretability-by-Design with Accurate Locally Additive Models and Conditional Feature Effects](https://mlsanity.com/paper/2602.16503/) (2026)

## Abstract (arXiv)

Supervised Machine Learning (SML) algorithms, such as Gradient Boosting, Random Forest, and Neural Networks, have become popular in recent years due to their superior predictive performance over traditional statistical methods. However, their complexity makes the results hard to interpret without additional tools. There has been a lot of recent work in developing global and local diagnostics for interpreting SML models. In this paper, we propose a locally-interpretable model that takes the fitted ML response surface, partitions the predictor space using model-based regression trees, and fits interpretable main-effects models at each of the nodes. We adapt the algorithm to be efficient in dealing with high-dimensional predictors. While the main focus is on interpretability, the resulting surrogate model also has reasonably good predictive performance.