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Modelling children ever born using performance evaluation metrics: A dataset
Predicting the number of total children ever born in a country is a key component for proper implementation of economic growth policy. Here, performance metrics were used to predict models that appropriately describe the factors that affect children ever born. A comparison of 60% training and 40% va...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8134711/ https://www.ncbi.nlm.nih.gov/pubmed/34026975 http://dx.doi.org/10.1016/j.dib.2021.107077 |
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author | Ibeji, Jecinta U. Zewotir, Temesgen North, Delia Amusa, Lateef |
author_facet | Ibeji, Jecinta U. Zewotir, Temesgen North, Delia Amusa, Lateef |
author_sort | Ibeji, Jecinta U. |
collection | PubMed |
description | Predicting the number of total children ever born in a country is a key component for proper implementation of economic growth policy. Here, performance metrics were used to predict models that appropriately describe the factors that affect children ever born. A comparison of 60% training and 40% validation, 70% training and 30% validation, 80% training and 20% validation also 90% training and 10% validation was performed respectively to examine the three models’ behaviours (Poisson regression, Negative Binomial regression and Generalized Poisson regression) with RMSE, R(2), MAE and MSE as performance metrics. Although all the three models had almost identical performance evaluation metrics, the Poisson regression was chosen as the most appropriate model because it is the simplest model. |
format | Online Article Text |
id | pubmed-8134711 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-81347112021-05-21 Modelling children ever born using performance evaluation metrics: A dataset Ibeji, Jecinta U. Zewotir, Temesgen North, Delia Amusa, Lateef Data Brief Data Article Predicting the number of total children ever born in a country is a key component for proper implementation of economic growth policy. Here, performance metrics were used to predict models that appropriately describe the factors that affect children ever born. A comparison of 60% training and 40% validation, 70% training and 30% validation, 80% training and 20% validation also 90% training and 10% validation was performed respectively to examine the three models’ behaviours (Poisson regression, Negative Binomial regression and Generalized Poisson regression) with RMSE, R(2), MAE and MSE as performance metrics. Although all the three models had almost identical performance evaluation metrics, the Poisson regression was chosen as the most appropriate model because it is the simplest model. Elsevier 2021-04-22 /pmc/articles/PMC8134711/ /pubmed/34026975 http://dx.doi.org/10.1016/j.dib.2021.107077 Text en © 2021 The Author(s). Published by Elsevier Inc. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Data Article Ibeji, Jecinta U. Zewotir, Temesgen North, Delia Amusa, Lateef Modelling children ever born using performance evaluation metrics: A dataset |
title | Modelling children ever born using performance evaluation metrics: A dataset |
title_full | Modelling children ever born using performance evaluation metrics: A dataset |
title_fullStr | Modelling children ever born using performance evaluation metrics: A dataset |
title_full_unstemmed | Modelling children ever born using performance evaluation metrics: A dataset |
title_short | Modelling children ever born using performance evaluation metrics: A dataset |
title_sort | modelling children ever born using performance evaluation metrics: a dataset |
topic | Data Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8134711/ https://www.ncbi.nlm.nih.gov/pubmed/34026975 http://dx.doi.org/10.1016/j.dib.2021.107077 |
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