We include 1) data cleaning including variable scaling, missing values and unbalanced variables identification and removing, and strategies for variable balance improving; 2) modeling based on random forest and gradient boosted model including feature selection, model training, cross-validation and external testing. For more information, please see Deng X (2021). <doi:10.1016/j.scitotenv.2020.144746>; H2O.ai (Oct. 2016). R Interface for H2O, R package version 3.10.0.8. <https://github.com/h2oai/h2o-3>; Zhang W (2016). <doi:10.1016/j.scitotenv.2016.02.023>.
Package details |
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Author | Xinlei Deng [aut, cre, cph], Wangjian Zhang [aut], Tianyue Mi [aut], Shao Lin [aut] |
Maintainer | Xinlei Deng <xinlei.deng.apha@gmail.com> |
License | GPL-3 |
Version | 0.0.5 |
Package repository | View on CRAN |
Installation |
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