  Machine learning (ML) techniques applied to quantum many-body physics have
emerged as a new research field. While the numerical power of this approach is
undeniable, the most expressive ML algorithms, such as neural networks, are
black boxes: The user does neither know the logic behind the model predictions
nor the uncertainty of the model predictions. In this work, we present a
toolbox for interpretability and reliability, agnostic of the model
architecture. In particular, it provides a notion of the influence of the input
data on the prediction at a given test point, an estimation of the uncertainty
of the model predictions, and an extrapolation score for the model predictions.
Such a toolbox only requires a single computation of the Hessian of the
training loss function. Our work opens the road to the systematic use of
interpretability and reliability methods in ML applied to physics and, more
generally, science.
