# Interpretable Machine Learning with an Ensemble of Gradient Boosting Machines

arXiv: https://arxiv.org/abs/2010.07388 · HTML: https://mlsanity.com/paper/2010.07388/

## Key facts

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

## Built on by (most cited first, 14 of 14)

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- [Implementing local-explainability in Gradient Boosting Trees: Feature Contribution](https://mlsanity.com/paper/2402.09197/) (2024)
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- [Customer Churn Prediction Model using Explainable Machine Learning](https://mlsanity.com/paper/2303.00960/) (2023)
- [SurvBeNIM: The Beran-Based Neural Importance Model for Explaining the Survival Models](https://mlsanity.com/paper/2312.06638/) (2023)
- [Dual feature-based and example-based explanation methods](https://mlsanity.com/paper/2401.16294/) (2024)
- [Kinematic-Based Assessment of Surgical Actions in Microanastomosis](https://mlsanity.com/paper/2512.23942/) (2025)
- [ParamBoost: Gradient Boosted Piecewise Cubic Polynomials](https://mlsanity.com/paper/2604.18864/) (2026)
- [An Integrated Video-AI Platform for Action-Level Microanastomosis Training and Performance Feedback](https://mlsanity.com/paper/2609.06380/) (2026)

## Abstract (arXiv)

A method for the local and global interpretation of a black-box model on the basis of the well-known generalized additive models is proposed. It can be viewed as an extension or a modification of the algorithm using the neural additive model. The method is based on using an ensemble of gradient boosting machines (GBMs) such that each GBM is learned on a single feature and produces a shape function of the feature. The ensemble is composed as a weighted sum of separate GBMs resulting a weighted sum of shape functions which form the generalized additive model. GBMs are built in parallel using randomized decision trees of depth 1, which provide a very simple architecture. Weights of GBMs as well as features are computed in each iteration of boosting by using the Lasso method and then updated by means of a specific smoothing procedure. In contrast to the neural additive model, the method provides weights of features in the explicit form, and it is simply trained. A lot of numerical experiments with an algorithm implementing the proposed method on synthetic and real datasets demonstrate its efficiency and properties for local and global interpretation.