Package: important 0.2.1.9000

Max Kuhn

important: Supervised Feature Selection

Interfaces for choosing important predictors in supervised regression, classification, and censored regression models. Permuted importance scores (Biecek and Burzykowski (2021) <doi:10.1201/9780429027192>) can be computed for 'tidymodels' model fits.

Authors:Max Kuhn [aut, cre], Posit Software, PBC [cph, fnd]

important_0.2.1.9000.tar.gz
important_0.2.1.9000.zip(r-4.7)important_0.2.1.9000.zip(r-4.6)important_0.2.1.9000.zip(r-4.5)
important_0.2.1.9000.tgz(r-4.6-any)important_0.2.1.9000.tgz(r-4.5-any)
important_0.2.1.9000.tar.gz(r-4.7-any)important_0.2.1.9000.tar.gz(r-4.6-any)
important_0.2.1.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
important/json (API)

# Install 'important' in R:

install.packages('important', repos = c('https://tidymodels.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/tidymodels/important/issues

Pkgdown/docs site:https://important.tidymodels.org

On CRAN:

5.10 score 19 stars 33 scripts 263 downloads 7 exports 105 dependencies

Last updated from:4490a0c9be. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	230
source / vignettes	OK	226
linux-release-x86_64	OK	224
macos-release-arm64	OK	158
macos-oldrel-arm64	OK	174
windows-devel	OK	159
windows-release	OK	153
windows-oldrel	OK	145
wasm-release	OK	148

Exports:augment autoplot importance_perm required_pkgs step_predictor_best step_predictor_desirability step_predictor_retain

Dependencies:base64enc bslib cachem class cli clock codetools cpp11 data.table desirability2 diagram dials DiceDesign digest dplyr evaluate farver fastmap filtro fontawesome fs furrr future future.apply GauPro generics ggplot2 globals glue gower gtable hardhat highr htmltools ipred isoband jquerylib jsonlite KernSmooth knitr labeling lattice lava lbfgs lifecycle listenv lubridate magrittr MASS Matrix memoise mime mixopt modelenv nnet numDeriv parallelly parsnip pillar pkgconfig prettyunits pROC prodlim progressr purrr R6 rappdirs RColorBrewer Rcpp RcppArmadillo recipes rlang rmarkdown rpart rsample S7 sass scales sfd shape slider sparsevctrs splitfngr SQUAREM stringi stringr survival tailor tibble tidyr tidyselect timechange timeDate tinytex tune tzdb utf8 vctrs viridisLite warp withr workflows xfun yaml yardstick

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Visualize importance scores	autoplot.importance_perm
Compute permutation-based predictor importance	importance_perm
Supervised Feature Selection via Choosing the Top Predictors	step_predictor_best
Supervised Multivariate Feature Selection via Desirability Functions	step_predictor_desirability
Supervised Feature Selection via A Single Filter	step_predictor_retain