  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.
