Optimal Counterfactual Explanations for Scorecard modelling
- URL: http://arxiv.org/abs/2104.08619v1
- Date: Sat, 17 Apr 2021 18:51:50 GMT
- Title: Optimal Counterfactual Explanations for Scorecard modelling
- Authors: Guillermo Navas-Palencia
- Abstract summary: In this work, we investigate mathematical programming formulations for scorecard models.
The proposed mixed-integer programming formulations combine objective functions to ensure close, realistic and sparse counterfactuals.
Experiments on two real-world datasets confirm that the presented approach can generate optimal diverse counterfactuals.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Counterfactual explanations is one of the post-hoc methods used to provide
explainability to machine learning models that have been attracting attention
in recent years. Most examples in the literature, address the problem of
generating post-hoc explanations for black-box machine learning models after
the rejection of a loan application. In contrast, in this work, we investigate
mathematical programming formulations for scorecard models, a type of
interpretable model predominant within the banking industry for lending. The
proposed mixed-integer programming formulations combine objective functions to
ensure close, realistic and sparse counterfactuals using multi-objective
optimization techniques for a binary, probability or continuous outcome.
Moreover, we extend these formulations to generate multiple optimal
counterfactuals simultaneously while guaranteeing diversity. Experiments on two
real-world datasets confirm that the presented approach can generate optimal
diverse counterfactuals addressing desired properties with assumable CPU times
for practice use.
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