  Counterfactual explanations are viewed as an effective way to explain machine
learning predictions. This interest is reflected by a relatively young
literature with already dozens of algorithms aiming to generate such
explanations. These algorithms are focused on finding how features can be
modified to change the output classification. However, this rather general
objective can be achieved in different ways, which brings about the need for a
methodology to test and benchmark these algorithms. The contributions of this
work are manifold: First, a large benchmarking study of 10 algorithmic
approaches on 22 tabular datasets is performed, using 9 relevant evaluation
metrics. Second, the introduction of a novel, first of its kind, framework to
test counterfactual generation algorithms. Third, a set of objective metrics to
evaluate and compare counterfactual results. And finally, insight from the
benchmarking results that indicate which approaches obtain the best performance
on what type of dataset. This benchmarking study and framework can help
practitioners in determining which technique and building blocks most suit
their context, and can help researchers in the design and evaluation of current
and future counterfactual generation algorithms. Our findings show that,
overall, there's no single best algorithm to generate counterfactual
explanations as the performance highly depends on properties related to the
dataset, model, score and factual point specificities.

Machine Learning;Artificial Intelligence