tailor: Iterative Steps for Postprocessing Model Predictions
Postprocessors refine predictions outputted from machine
learning models to improve predictive performance or better satisfy
distributional limitations. This package introduces 'tailor' objects,
which compose iterative adjustments to model predictions. A number of
pre-written adjustments are provided with the package, such as
calibration. See Lichtenstein, Fischhoff, and Phillips (1977)
<doi:10.1007/978-94-010-1276-8_19>. Other methods and utilities to compose
new adjustments are also included. Tailors are tightly integrated with the
'tidymodels' framework.
| Version: |
0.1.0 |
| Depends: |
R (≥ 4.1) |
| Imports: |
cli, dplyr, generics, hardhat, purrr, rlang (≥ 1.1.0), tibble, tidyselect, vctrs |
| Suggests: |
betacal, dials (≥ 1.4.1), mgcv, modeldata, probably (≥
1.1.0), testthat (≥ 3.0.0) |
| Published: |
2025-08-25 |
| Author: |
Simon Couch [aut],
Hannah Frick [aut],
Emil HvitFeldt [aut],
Max Kuhn  [aut,
cre],
Posit Software, PBC  [cph,
fnd] |
| Maintainer: |
Max Kuhn <max at posit.co> |
| BugReports: |
https://github.com/tidymodels/tailor/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/tidymodels/tailor,
https://tailor.tidymodels.org |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
tailor results |
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