Package: caretEnsemble 4.0.2

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.zip(r-4.7)caretEnsemble_4.0.2.zip(r-4.6)caretEnsemble_4.0.2.zip(r-4.5)
caretEnsemble_4.0.2.tgz(r-4.6-any)caretEnsemble_4.0.2.tgz(r-4.5-any)
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://zachmayer.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:

12.66 score 229 stars 3 packages 1.1k scripts 1.5k downloads 8 mentions 14 exports 75 dependencies

Last updated from:eefb055bf2. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	328
source / vignettes	OK	257
linux-release-x86_64	OK	282
macos-release-arm64	OK	230
macos-oldrel-arm64	OK	364
windows-devel	OK	248
windows-release	OK	232
windows-oldrel	OK	216
wasm-release	OK	279

Exports:as.caretList caretEnsemble caretList caretModelSpec caretStack defaultControl defaultMetric extractMetric greedyMSE greedyMSE_caret permutationImportance plot_variable_importance tuneCheck wtd.sd

Dependencies:caret class cli clock codetools cpp11 data.table diagram digest dplyr e1071 farver foreach future future.apply generics ggplot2 globals glue gower gtable hardhat ipred isoband iterators KernSmooth labeling lattice lava lifecycle listenv lubridate magrittr MASS Matrix ModelMetrics nlme nnet numDeriv parallelly patchwork pbapply pillar pkgconfig plyr pROC prodlim progressr proxy purrr R6 RColorBrewer Rcpp recipes reshape2 rlang rpart S7 scales shape sparsevctrs SQUAREM stringi stringr survival tibble tidyr tidyselect timechange timeDate tzdb utf8 vctrs viridisLite withr

A Brief Introduction to caretEnsemble

caretList | caretEnsemble | caretStack

Rendered fromcaretEnsemble-intro.Rmdusingknitr::rmarkdown

Last update: 2024-08-13
Started: 2014-11-22

Version 4.0 New Features

Rendered fromVersion-4.0-New-Features.Rmdusingknitr::rmarkdown

Last update: 2024-08-13
Started: 2024-08-13

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Index a caretList	[.caretList
Add cross-group statistics to the importance table	add_cross_group_stats
Convert object to caretList object	as.caretList
Convert object to caretList object - For Future Use	as.caretList.default
Convert list to caretList	as.caretList.list
Convenience function for more in-depth diagnostic plots of caretStack objects	autoplot.caretStack
S3 definition for concatenating caretList	c.caretList
S3 definition for concatenating train objects	c.train
Combine several predictive models via weights	caretEnsemble-package caretEnsemble
Create a list of several train models from the caret package	caretList
Generate a specification for fitting a caret model	caretModelSpec
Combine several predictive models via stacking	caretStack
Construct a default train control for use with caretList	defaultControl
Construct a default metric	defaultMetric
Comparison dotplot for a caretStack object	dotplot.caretStack
Generic function to extract accuracy metrics from various model objects	extractMetric
Extract accuracy metrics from a 'caretList' object	extractMetric.caretList
Extract accuracy metrics from a 'caretStack' object	extractMetric.caretStack
Extract accuracy metrics from a 'train' model	extractMetric.train
Greedy optimization for MSE	greedyMSE
caret interface for greedyMSE	greedyMSE_caret
Permutation Importance	permutationImportance
Plot a group of variable importances	plot_group
Plot Variable Importance from a caretStack Model	plot_variable_importance
Plot a caretList object	plot.caretList
Plot a caretStack object	plot.caretStack
Create a matrix of predictions for each of the models in a caretList	predict.caretList
Make predictions from a caretStack	predict.caretStack
Predict method for greedyMSE	predict.greedyMSE
Prepare variable importance data.table from a caretStack	prepare_importance
Print a caretStack object	print.caretStack
Print method for greedyMSE	print.greedyMSE
Print a summary.caretList object	print.summary.caretList
Print a summary.caretStack object	print.summary.caretStack
Summarize a caretList	summary.caretList
Summarize a caretStack object	summary.caretStack
Check that the tuning parameters list supplied by the user is valid	tuneCheck
Variable importance for caretStack	varImp.caretStack
variable importance for a greedyMSE model	varImp.greedyMSE
Calculate a weighted standard deviation	wtd.sd