Interpretability in Symbolic Regression: a benchmark of Explanatory Methods using the Feynman data set

arXiv:2404.05908v1 Announce Type: new
Abstract: In some situations, the interpretability of the machine learning models plays a role as important as the model accuracy. Interpretability comes from the need to trust the prediction model, verify some of its properties, or even enforce them to improve fairness. Many model-agnostic explanatory methods exists to provide explanations for black-box models. In the regression task, the practitioner can use white-boxes or gray-boxes models to achieve more interpretable results, which is the case of symbolic …

abstract accuracy arxiv benchmark cs.ai cs.lg data data set fairness feynman interpretability machine machine learning machine learning models model accuracy prediction regression role set them trust type verify