Is poverty predictable with machine learning? : a study of DHS data from Kyrgyzstan
Qing Li, Shuai Yu, Damien Échevin, Min Fan
| Year of publication: |
2022
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| Authors: | Li, Qing ; Yu, Shuai ; Echevin, Damien ; Fan, Min |
| Published in: |
Socio-economic planning sciences : the international journal of public sector decision-making. - Amsterdam [u.a.] : Elsevier, ISSN 0038-0121, ZDB-ID 208905-1. - Vol. 81.2022, p. 1-9
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| Subject: | Generalized linear model | Machine learning | Poverty prediction | XGBoost | Künstliche Intelligenz | Artificial intelligence | Armut | Poverty | Prognoseverfahren | Forecasting model | Kirgisistan | Kyrgyzstan | Schätztheorie | Estimation theory |
| Type of publication: | Article |
|---|---|
| Type of publication (narrower categories): | Aufsatz in Zeitschrift ; Article in journal |
| Language: | English |
| Other identifiers: | 10.1016/j.seps.2021.101195 [DOI] |
| Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10013255804