vtreat: A Statistically Sound 'data.frame' Processor/Conditioner
A 'data.frame' processor/conditioner that prepares real-world data for predictive modeling in a statistically sound manner.
'vtreat' prepares variables so that data has fewer exceptional cases, making
it easier to safely use models in production. Common problems 'vtreat' defends
against: 'Inf', 'NA', too many categorical levels, rare categorical levels, and new
categorical levels (levels seen during application, but not during training). Reference:
"'vtreat': a data.frame Processor for Predictive Modeling", Zumel, Mount, 2016, <doi:10.5281/zenodo.1173313>.
| Version: |
1.6.5 |
| Depends: |
R (≥ 3.4.0), wrapr (≥ 2.1.0) |
| Imports: |
stats, digest |
| Suggests: |
rquery (≥ 1.4.99), rqdatatable (≥ 1.3.3), data.table (≥
1.12.2), knitr, rmarkdown, parallel, DBI, RSQLite, datasets, R.rsp, tinytest |
| Published: |
2024-06-12 |
| DOI: |
10.32614/CRAN.package.vtreat |
| Author: |
John Mount [aut, cre],
Nina Zumel [aut],
Win-Vector LLC [cph] |
| Maintainer: |
John Mount <jmount at win-vector.com> |
| BugReports: |
https://github.com/WinVector/vtreat/issues |
| License: |
GPL-2 | GPL-3 |
| URL: |
https://github.com/WinVector/vtreat/,
https://winvector.github.io/vtreat/ |
| NeedsCompilation: |
no |
| Materials: |
README NEWS |
| CRAN checks: |
vtreat results |
Documentation:
Downloads:
Reverse dependencies:
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