Package: baguette 1.1.0.9000

Max Kuhn

baguette: Efficient Model Functions for Bagging

Tree- and rule-based models can be bagged (<doi:10.1007/BF00058655>) using this package and their predictions equations are stored in an efficient format to reduce the model objects size and speed.

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

baguette_1.1.0.9000.tar.gz
baguette_1.1.0.9000.zip(r-4.7)baguette_1.1.0.9000.zip(r-4.6)baguette_1.1.0.9000.zip(r-4.5)
baguette_1.1.0.9000.tgz(r-4.6-any)baguette_1.1.0.9000.tgz(r-4.5-any)
baguette_1.1.0.9000.tar.gz(r-4.7-any)baguette_1.1.0.9000.tar.gz(r-4.6-any)
baguette_1.1.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
baguette/json (API)

# Install 'baguette' in R:
install.packages('baguette', repos = c('https://tidymodels.r-universe.dev', 'https://cloud.r-project.org'))

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

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

On CRAN:

Conda:

7.10 score 29 stars 972 scripts 810 downloads 6 exports 63 dependencies

Last updated from:237f2e6df0. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK212
source / vignettesOK222
linux-release-x86_64OK212
macos-release-arm64OK123
macos-oldrel-arm64OK113
windows-develOK151
windows-releaseOK158
windows-oldrelOK141
wasm-releaseOK150

Exports:baggerclass_costcontrol_bagnnet_imp_garsonvar_impvar_imp.bagger

Dependencies:butcherC50clicodetoolscpp11crayonCubistdialsDiceDesigndigestdplyrfarverFormulafurrrfuturegenericsggplot2globalsgluegtablehardhatinumisobandlabelinglatticelibcoinlifecyclelistenvlobstrmagrittrMatrixmvtnormparallellyparsnippartykitpillarpkgconfigplyrprettyunitspurrrR6RColorBrewerRcppreshape2rlangrpartrsampleS7scalessfdslidersparsevctrsstringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewarpwithr

Readme and manuals

Help Manual

Help pageTopics
Bagging functionsbagger bagger.data.frame bagger.default bagger.formula bagger.matrix bagger.recipe
Cost parameter for minority classclass_cost
Controlling the bagging processcontrol_bag
Predictions from a bagged modelpredict.bagger
Obtain variable importance scoresvar_imp.bagger