A tree based eXtreme Gradient Boosting (XGBoost) machine learning model to forecast the annual rice production in Bangladesh
- DOI
- 10.1371/journal.pone.0283452
- Published
- 2023-03-27
- Container
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1371/journal.pone.0283452,
title = {A tree based eXtreme Gradient Boosting (XGBoost) machine learning model to forecast the annual rice production in Bangladesh},
author = {Mst Noorunnahar and Arman Hossain Chowdhury and Farhana Arefeen Mila},
year = {2023},
journal = {PLOS ONE},
doi = {10.1371/journal.pone.0283452},
url = {https://doi.org/10.1371/journal.pone.0283452}
}RIS
TY - JOUR TI - A tree based eXtreme Gradient Boosting (XGBoost) machine learning model to forecast the annual rice production in Bangladesh AU - Mst Noorunnahar AU - Arman Hossain Chowdhury AU - Farhana Arefeen Mila PY - 2023 JO - PLOS ONE DO - 10.1371/journal.pone.0283452 UR - https://doi.org/10.1371/journal.pone.0283452 ER -
APA
Noorunnahar, M., Chowdhury, A. H., & Mila, F. A. (2023). A tree based eXtreme Gradient Boosting (XGBoost) machine learning model to forecast the annual rice production in Bangladesh. PLOS ONE. https://doi.org/10.1371/journal.pone.0283452
Source records
- crossref · retrieved 2026-09-26T09:35:51.329Z