APML: An Approach for Machine-Learning Modelling

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 <https://github.com/h2oai/h2o-3>; Zhang W (2016). <doi:10.1016/j.scitotenv.2016.02.023>.

Version: 0.0.5
Imports: survival, h2o, performanceEstimation, fastDummies, dplyr, ggplot2, pROC
Published: 2022-05-12
Author: Xinlei Deng [aut, cre, cph], Wangjian Zhang [aut], Tianyue Mi [aut], Shao Lin [aut]
Maintainer: Xinlei Deng <xinlei.deng.apha at gmail.com>
License: GPL-3
NeedsCompilation: no
CRAN checks: APML results


Reference manual: APML.pdf


Package source: APML_0.0.5.tar.gz
Windows binaries: r-devel: APML_0.0.5.zip, r-release: APML_0.0.5.zip, r-oldrel: APML_0.0.5.zip
macOS binaries: r-release (arm64): APML_0.0.5.tgz, r-oldrel (arm64): APML_0.0.5.tgz, r-release (x86_64): APML_0.0.5.tgz, r-oldrel (x86_64): APML_0.0.5.tgz
Old sources: APML archive


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