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✨
card.svg |card.png
baguette/json (API)
NEWS

# 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:

7.00 score 28 stars 802 scripts 848 downloads 6 exports 63 dependencies

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

TargetResultTimeFilesSyslog
linux-devel-x86_64OK207
source / vignettesOK222
linux-release-x86_64OK203
macos-release-arm64OK179
macos-oldrel-arm64OK106
windows-develOK159
windows-releaseOK144
windows-oldrelOK147
wasm-releaseOK131

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