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 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

Downloads:

Reference manual: APML.pdf
Package source: APML_0.0.2.tar.gz
Windows binaries: r-devel: APML_0.0.2.zip, r-release: APML_0.0.2.zip, 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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