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Automated Learning of Interpretable Models with Quantified Uncertainty
[article]
2022
arXiv
pre-print
Interpretability and uncertainty quantification in machine learning can provide justification for decisions, promote scientific discovery and lead to a better understanding of model behavior. Symbolic regression provides inherently interpretable machine learning, but relatively little work has focused on the use of symbolic regression on noisy data and the accompanying necessity to quantify uncertainty. A new Bayesian framework for genetic-programming-based symbolic regression (GPSR) is
arXiv:2205.01626v1
fatcat:ggxhsptf4zc23ayvvvgomladwi