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: |
0.0.5 |
| 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 |
Documentation:
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