  Multidimensional efficiency maps are commonly used in high energy physics
experiments to mitigate the limitations in the generation of large samples of
simulated events. Binned multidimensional efficiency maps are however strongly
limited by statistics. We propose a neural network approach to learn ratios of
local densities to estimate in an optimal fashion efficiencies as a function of
a set of parameters. Graph neural network techniques are used to account for
the high dimensional correlations between different physics objects in the
event. We show in a specific toy model how this method is applicable to produce
accurate multidimensional efficiency maps for heavy flavor tagging classifiers
in HEP experiments, including for processes on which it was not trained.
