preprints_ui: vz496_v1
Data license: ODbL (database) & original licenses (content) · Data source: Open Science Framework
| id | title | description | date_created | date_modified | date_published | original_publication_date | publication_doi | provider | is_published | reviews_state | version | is_latest_version | preprint_doi | license | tags_list | tags_data | contributors_list | contributors_data | first_author | subjects_list | subjects_data | download_url | has_coi | conflict_of_interest_statement | has_data_links | has_prereg_links | prereg_links | prereg_link_info | last_updated |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| vz496_v1 | Logistic Regression Teams Up With Artificial Intelligence To Beat Neural Network and Gradient Boosted Machine | 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 logistic regression models built from the ground up, beating an industry standard consumer credit risk score by five points. In a machine learning modeling project our reference logistic regression model beats out GBM, and has performance comparable to a NN model but much more amenable in explanation and generating reasons for adverse action. We also incorporate into our system a method to eliminate disparate impact used by the FRB and the FTC. | 2022-01-17T17:24:36.695470 | 2023-03-05T14:55:35.957099 | 2022-01-17T18:21:58.898819 | engrxiv | 0 | withdrawn | 1 | 1 | https://doi.org/10.31224/osf.io/vz496 | No license | AI; AIC; ARM; BISG; BLAS; CRA; FRB; FTC; Federal Reserve Board; Federal Trade Commission; GBM; GPGPU; GPU; Gini coefficient; IRLS; IV; Intel; KS; LAPACK; Logistic; ML; Modeling; NN; PSI; VIF; Wald chi squared; artificial intelligence; condition index; correlation coefficient; disparate impact; machine learning; neural network; normalization; proportion of variation; regression; reject inference; transformation | ["AI", "AIC", "ARM", "BISG", "BLAS", "CRA", "FRB", "FTC", "Federal Reserve Board", "Federal Trade Commission", "GBM", "GPGPU", "GPU", "Gini coefficient", "IRLS", "IV", "Intel", "KS", "LAPACK", "Logistic", "ML", "Modeling", "NN", "PSI", "VIF", "Wald chi squared", "artificial intelligence", "condition index", "correlation coefficient", "disparate impact", "machine learning", "neural network", "normalization", "proportion of variation", "regression", "reject inference", "transformation"] | Daniel M. Tom, Ph.D. | [{"id": "98gq4", "name": "Daniel M. Tom, Ph.D.", "index": 0, "orcid": "0000-0003-4853-2498", "bibliographic": true}] | Daniel M. Tom, Ph.D. | Engineering; Computational Engineering; Operations Research, Systems Engineering and Industrial Engineering; Risk Analysis | [{"id": "5994df7a54be8100732d43ae", "text": "Engineering"}, {"id": "5994df7a54be8100732d43c2", "text": "Computational Engineering"}, {"id": "5994df7a54be8100732d43c3", "text": "Operations Research, Systems Engineering and Industrial Engineering"}, {"id": "5994df7b54be8100732d43e8", "text": "Risk Analysis"}] | 0 | no | no | [] | 2025-04-09T20:03:40.641412 |