# Symbolic Knowledge Extraction from Opaque Predictors Applied to Cosmic-Ray Data Gathered with LISA Pathfinder

arXiv: https://arxiv.org/abs/2209.04697 · HTML: https://mlsanity.com/paper/2209.04697/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 35 references that are in the map.
- First posted on arXiv 10 Sep 2022.

## Builds on (most cited first, 3 of 3)

- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [Automatic Detection of Interplanetary Coronal Mass Ejections in Solar Wind In Situ Data](https://mlsanity.com/paper/2205.03578/) (2022)

## Built on by (most cited first, 3 of 3)

- [Evaluation Metrics for Symbolic Knowledge Extracted from Machine Learning Black Boxes: A Discussion Paper](https://mlsanity.com/paper/2211.00238/) (2022)
- [Solar Wind Speed Estimate with Machine Learning Ensemble Models for LISA](https://mlsanity.com/paper/2302.06740/) (2023)
- [Predicting the energetic proton flux with a machine learning regression algorithm](https://mlsanity.com/paper/2406.12730/) (2024)

## Abstract (arXiv)

Machine learning models are nowadays ubiquitous in space missions, performing a wide variety of tasks ranging from the prediction of multivariate time series through the detection of specific patterns in the input data. Adopted models are usually deep neural networks or other complex machine learning algorithms providing predictions that are opaque, i.e., human users are not allowed to understand the rationale behind the provided predictions. Several techniques exist in the literature to combine the impressive predictive performance of opaque machine learning models with human-intelligible prediction explanations, as for instance the application of symbolic knowledge extraction procedures. In this paper are reported the results of different knowledge extractors applied to an ensemble predictor capable of reproducing cosmic-ray data gathered on board the LISA Pathfinder space mission. A discussion about the readability/fidelity trade-off of the extracted knowledge is also presented.