Uplift modeling aims at predicting the causal effect of an action such as a medical treatment or a marketing campaign on a particular individual, by taking into consideration the response to a treatment. In order to simplify the task for practitioners in uplift modeling, we propose a combination of tools that can be separated into the following ingredients: i) quantization, ii) visualization, iii) feature engineering, iv) feature selection and, v) model validation. For more details, please read Belbahri et Al. (2019) <https://dms.umontreal.ca/~murua/research/UpliftRegression.pdf>.
| Version: | 0.1-1 |
| Depends: | R (≥ 3.1.2) |
| Imports: | dplyr, glmnet |
| Published: | 2019-01-29 |
| Author: | Mouloud Belbahri, Olivier Gandouet, Alejandro Murua, Vahid Partovi Nia |
| Maintainer: | Mouloud Belbahri <mouloud.belbahri at gmail.com> |
| License: | GPL-2 | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | tools4uplift results |
| Reference manual: | tools4uplift.pdf |
| Package source: | tools4uplift_0.1-1.tar.gz |
| Windows binaries: | r-devel: tools4uplift_0.1-1.zip, r-release: tools4uplift_0.1-1.zip, r-oldrel: tools4uplift_0.1-1.zip |
| macOS binaries: | r-release: tools4uplift_0.1-1.tgz, r-oldrel: tools4uplift_0.1-1.tgz |
| Old sources: | tools4uplift archive |
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