# Integrating White and Black Box Techniques for Interpretable Machine Learning

arXiv: https://arxiv.org/abs/2407.08973 · HTML: https://mlsanity.com/paper/2407.08973/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 12 Jul 2024.

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

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning](https://mlsanity.com/paper/1609.06570/) (2016)
- [Machine Learning with a Reject Option: A survey](https://mlsanity.com/paper/2107.11277/) (2021)
- [A Comparative Analysis of XGBoost](https://mlsanity.com/paper/1911.01914/) (2019)

## Built on by (most cited first, 1 of 1)

- [Let the Fuzzy Rule Speak: Enhancing In-context Learning Debiasing with Interpretability](https://mlsanity.com/paper/2412.19018/) (2024)

## Abstract (arXiv)

In machine learning algorithm design, there exists a trade-off between the interpretability and performance of the algorithm. In general, algorithms which are simpler and easier for humans to comprehend tend to show worse performance than more complex, less transparent algorithms. For example, a random forest classifier is likely to be more accurate than a simple decision tree, but at the expense of interpretability. In this paper, we present an ensemble classifier design which classifies easier inputs using a highly-interpretable classifier (i.e., white box model), and more difficult inputs using a more powerful, but less interpretable classifier (i.e., black box model).