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Article Dans Une Revue Expert Systems with Applications Année : 2020

When stakes are high: balancing accuracy and transparency with Model-Agnostic Interpretable Data-driven suRRogates

Roel Henckaerts
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Marie-Pier Côté
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Résumé

Highly regulated industries, like banking and insurance, ask for transparent decision-making algorithms. At the same time, competitive markets are pushing for the use of complex black box models. We therefore present a procedure to develop a Model-Agnostic Interpretable Data-driven suRRogate (maidrr) suited for structured tabular data. Knowledge is extracted from a black box via partial dependence effects. These are used to perform smart feature engineering by grouping variable values. This results in a segmentation of the feature space with automatic variable selection. A transparent generalized linear model (GLM) is fit to the features in categorical format and their relevant interactions. We demonstrate our R package maidrr with a case study on general insurance claim frequency modeling for six publicly available datasets. Our maidrr GLM closely approximates a gradient boosting machine (GBM) black box and outperforms both a linear and tree surrogate as benchmarks.
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Dates et versions

hal-04015711 , version 1 (06-03-2023)

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Roel Henckaerts, Katrien Antonio, Marie-Pier Côté. When stakes are high: balancing accuracy and transparency with Model-Agnostic Interpretable Data-driven suRRogates. Expert Systems with Applications, 2020, ⟨10.48550/arXiv.2007.06894⟩. ⟨hal-04015711⟩

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