Package: caretEnsemble 4.0.2

Zachary A. Deane-Mayer

caretEnsemble: Ensembles of Caret Models

Functions for creating ensembles of caret models: caretList() and caretStack(). caretList() is a convenience function for fitting multiple caret::train() models to the same dataset. caretStack() will make linear or non-linear combinations of these models, using a caret::train() model as a meta-model.

Authors:Zachary A. Deane-Mayer [aut, cre, cph], Jared E. Knowles [ctb], Antón López [ctb]

caretEnsemble_4.0.2.tar.gz
caretEnsemble_4.0.2.tar.gz(r-4.7-any)caretEnsemble_4.0.2.tar.gz(r-4.6-any)
caretEnsemble_4.0.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
caretEnsemble/json (API)

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

Bug tracker:https://github.com/zachmayer/caretensemble/issues

Pkgdown/docs site:https://zachmayer.github.io

On CRAN:

Conda:

6.65 score 2 stars 3 packages 1.1k scripts 1.5k downloads 8 mentions 14 exports 75 dependencies

Last updated from:f9867b0fd8. Checks:4 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK286
source / vignettesOK265
linux-release-x86_64OK256
wasm-releaseOK198

Exports:as.caretListcaretEnsemblecaretListcaretModelSpeccaretStackdefaultControldefaultMetricextractMetricgreedyMSEgreedyMSE_caretpermutationImportanceplot_variable_importancetuneCheckwtd.sd

Dependencies:caretclasscliclockcodetoolscpp11data.tablediagramdigestdplyre1071farverforeachfuturefuture.applygenericsggplot2globalsgluegowergtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixModelMetricsnlmennetnumDerivparallellypatchworkpbapplypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6RColorBrewerRcpprecipesreshape2rlangrpartS7scalesshapesparsevctrsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr

A Brief Introduction to caretEnsemble
caretList | caretEnsemble | caretStack
Rendered fromcaretEnsemble-intro.Rmdusingknitr::rmarkdown

Last update: 2024-08-18
Started: 2015-01-16

Version 4.0 New Features
Multiclass support | Greedy Optimizer in caretEnsemble | Enhanced S3 Methods | Improved Default trainControl | Mixed Resampling Strategies | Mixed Model Types | Transfer Learning | Permutation Importance
Rendered fromVersion-4.0-New-Features.Rmdusingknitr::rmarkdown

Last update: 2024-08-18
Started: 2024-08-18

Readme and manuals

Help Manual

Help pageTopics
Index a caretList[.caretList
Add cross-group statistics to the importance tableadd_cross_group_stats
Convert object to caretList objectas.caretList
Convert object to caretList object - For Future Useas.caretList.default
Convert list to caretListas.caretList.list
Convenience function for more in-depth diagnostic plots of caretStack objectsautoplot.caretStack
S3 definition for concatenating caretListc.caretList
S3 definition for concatenating train objectsc.train
Combine several predictive models via weightscaretEnsemble-package caretEnsemble
Create a list of several train models from the caret packagecaretList
Generate a specification for fitting a caret modelcaretModelSpec
Combine several predictive models via stackingcaretStack
Construct a default train control for use with caretListdefaultControl
Construct a default metricdefaultMetric
Comparison dotplot for a caretStack objectdotplot.caretStack
Generic function to extract accuracy metrics from various model objectsextractMetric
Extract accuracy metrics from a 'caretList' objectextractMetric.caretList
Extract accuracy metrics from a 'caretStack' objectextractMetric.caretStack
Extract accuracy metrics from a 'train' modelextractMetric.train
Greedy optimization for MSEgreedyMSE
caret interface for greedyMSEgreedyMSE_caret
Permutation ImportancepermutationImportance
Plot a group of variable importancesplot_group
Plot Variable Importance from a caretStack Modelplot_variable_importance
Plot a caretList objectplot.caretList
Plot a caretStack objectplot.caretStack
Create a matrix of predictions for each of the models in a caretListpredict.caretList
Make predictions from a caretStackpredict.caretStack
Predict method for greedyMSEpredict.greedyMSE
Prepare variable importance data.table from a caretStackprepare_importance
Print a caretStack objectprint.caretStack
Print method for greedyMSEprint.greedyMSE
Print a summary.caretList objectprint.summary.caretList
Print a summary.caretStack objectprint.summary.caretStack
Summarize a caretListsummary.caretList
Summarize a caretStack objectsummary.caretStack
Check that the tuning parameters list supplied by the user is validtuneCheck
Variable importance for caretStackvarImp.caretStack
variable importance for a greedyMSE modelvarImp.greedyMSE
Calculate a weighted standard deviationwtd.sd