Logistic Regression Collaborating with AI Beam Search

Daniel Tom

MPRA Paper from University Library of Munich, Germany

Abstract: We systematically explore the universe of all models using AI search methods. We automate much of the data preparation and testing of each model built along the way. The result is a method and system that generate superior production ready logistic regression models, beating an industry standard consumer credit risk score, GBM and NN ML models. We also incorporate into our system a method to eliminate disparate impact used by the FRB and the FTC.

Keywords: Modeling; Regression; Logistic; AIC; IRLS; AI; ML; NN; GBM; KS; IV; GC; Wald; X2; PSI; VIF; correlation coefficient; condition index; proportion-ofvariation; reject inference; FRB; FTC; CRA; disparate impact; BISG; SBC; ARM; Intel; GPU; GPGPU; BLAS; LAPACK; transformation; normalization (search for similar items in EconPapers)
JEL-codes: C61 (search for similar items in EconPapers)
Date: 2021-12-25, Revised 2023-03-04
New Economics Papers: this item is included in nep-cmp
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:116592

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