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>.

Getting started

Package details

AuthorXinlei Deng [aut, cre, cph], Wangjian Zhang [aut], Tianyue Mi [aut], Shao Lin [aut]
MaintainerXinlei Deng <xinlei.deng.apha@gmail.com>
LicenseGPL-3
Version0.0.5
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("APML")

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APML documentation built on May 12, 2022, 9:06 a.m.