  In this paper we suggest NICE: a new algorithm to generate counterfactual
explanations for heterogeneous tabular data. The design of our algorithm
specifically takes into account algorithmic requirements that often emerge in
real-life deployments: the ability to provide an explanation for all
predictions, being efficient in run-time, and being able to handle any
classification model (also non-differentiable ones). More specifically, our
approach exploits information from a nearest instance tospeed up the search
process. We propose four versions of NICE, where three of them optimize the
explanations for one of the following properties: sparsity, proximity or
plausibility. An extensive empirical comparison on 10 datasets shows that our
algorithm performs better on all properties than the current state-of-the-art.
These analyses show a trade-off between on the one hand plausiblity and on the
other hand proximity or sparsity, with our different optimization methods
offering the choice to select the preferred trade-off. An open-source
implementation of NICE can be found at https://github.com/ADMAntwerp/NICE.
