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>.
| Version: | 0.0.2 |
| Imports: | tidyverse, h2o, performanceEstimation, dummies, dplyr, ggplot2, pROC, survival |
| Published: | 2021-06-27 |
| Author: | Xinlei Deng [aut, cre, cph], Wangjian Zhang [aut], Shao Lin [aut] |
| Maintainer: | Xinlei Deng <xdeng3 at albany.edu> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | APML results |
| Reference manual: | APML.pdf |
| Package source: | APML_0.0.2.tar.gz |
| Windows binaries: | r-devel: APML_0.0.2.zip, r-release: not available, r-oldrel: APML_0.0.2.zip |
| macOS binaries: | r-release (arm64): APML_0.0.2.tgz, r-release (x86_64): APML_0.0.2.tgz, r-oldrel: APML_0.0.2.tgz |
| Old sources: | APML archive |
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