# Discussion: Effective and Interpretable Outcome Prediction by Training Sparse Mixtures of Linear Experts

arXiv: https://arxiv.org/abs/2407.13526 · HTML: https://mlsanity.com/paper/2407.13526/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 18 Jul 2024.

## Builds on (most cited first, 2 of 2)

- [Interpretable Mixture of Experts](https://mlsanity.com/paper/2206.02107/) (2022)
- [Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful Models](https://mlsanity.com/paper/2203.16073/) (2022)

## Abstract (arXiv)

Process Outcome Prediction entails predicting a discrete property of an unfinished process instance from its partial trace. High-capacity outcome predictors discovered with ensemble and deep learning methods have been shown to achieve top accuracy performances, but they suffer from a lack of transparency. Aligning with recent efforts to learn inherently interpretable outcome predictors, we propose to train a sparse Mixture-of-Experts where both the ``gate'' and ``expert'' sub-nets are Logistic Regressors. This ensemble-like model is trained end-to-end while automatically selecting a subset of input features in each sub-net, as an alternative to the common approach of performing a global feature selection step prior to model training. Test results on benchmark logs confirmed the validity and efficacy of this approach.