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

• URL: http://arxiv.org/abs/2007.06894v2
• Date: Thu, 10 Dec 2020 17:44:03 GMT
• Title: When stakes are high: balancing accuracy and transparency with Model-Agnostic Interpretable Data-driven suRRogates
• Authors: Roel Henckaerts and Katrien Antonio and Marie-Pier C\^ot\'e
• Abstract summary: Highly regulated industries, like banking and insurance, ask for transparent decision-making algorithms. We present a procedure to develop a Model-Agnostic Interpretable Data-driven suRRogate (maidrr) Knowledge is extracted from a black box via partial dependence effects. This results in a segmentation of the feature space with automatic variable selection.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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