# Improved prediction rule ensembling through model-based data generation

arXiv: https://arxiv.org/abs/2109.13672 · HTML: https://mlsanity.com/paper/2109.13672/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 28 Sep 2021.

## Builds on (most cited first, 5 of 5)

- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [RuleMatrix: Visualizing and Understanding Classifiers with Rules](https://mlsanity.com/paper/1807.06228/) (2018)
- [Gradient and Newton Boosting for Classification and Regression](https://mlsanity.com/paper/1808.03064/) (2018)
- [Estimation Stability with Cross Validation (ESCV)](https://mlsanity.com/paper/1303.3128/) (2013)
- [Fitting Prediction Rule Ensembles with R Package pre](https://mlsanity.com/paper/1707.07149/) (2017)

## Abstract (arXiv)

Prediction rule ensembles (PRE) provide interpretable prediction models with relatively high accuracy.PRE obtain a large set of decision rules from a (boosted) decision tree ensemble, and achieves sparsitythrough application of Lasso-penalized regression. This article examines the use of surrogate modelsto improve performance of PRE, wherein the Lasso regression is trained with the help of a massivedataset generated by the (boosted) decision tree ensemble. This use of model-based data generationmay improve the stability and consistency of the Lasso step, thus leading to improved overallperformance. We propose two surrogacy approaches, and evaluate them on simulated and existingdatasets, in terms of sparsity and predictive accuracy. The results indicate that the use of surrogacymodels can substantially improve the sparsity of PRE, while retaining predictive accuracy, especiallythrough the use of a nested surrogacy approach.