Model stacking is an ensemble technique
that involves training a model to combine the outputs of many
diverse statistical models, and has been shown to improve
predictive performance in a variety of settings. 'stacks'
implements a grammar for 'tidymodels'-aligned model stacking.
| Version: |
1.0.0 |
| Depends: |
R (≥ 2.10) |
| Imports: |
tune (≥ 0.1.3), dplyr (≥ 1.0.0), rlang (≥ 0.4.0), tibble (≥ 2.1.3), purrr (≥ 0.3.2), parsnip (≥ 0.0.4), workflows (≥
0.2.3), recipes (≥ 0.2.0), rsample (≥ 0.1.1), workflowsets (≥ 0.1.0), butcher (≥ 0.1.3), yardstick, tidyr, glue, ggplot2, glmnet, cli, stats, foreach |
| Suggests: |
testthat (≥ 3.0.0), covr, kknn, ranger, knitr, modeldata, rmarkdown, nnet, kernlab, mockr, h2o, SuperLearner |
| Published: |
2022-07-06 |
| Author: |
Simon Couch [aut, cre],
Max Kuhn [aut],
RStudio [cph, fnd] |
| Maintainer: |
Simon Couch <simonpatrickcouch at gmail.com> |
| BugReports: |
https://github.com/tidymodels/stacks/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://stacks.tidymodels.org/,
https://github.com/tidymodels/stacks |
| NeedsCompilation: |
no |
| Materials: |
README NEWS |
| CRAN checks: |
stacks results |