Package: metaEnsembleR 0.1.0

metaEnsembleR: Intuitive Package for Meta-Ensemble Learning (Classification, Regression) that is Fully-Automated

This package significantly lowers the barrier for the practitioners to apply heterogeneous ensemble learning techniques in an amateur fashion to their everyday predictive problems.

Authors:Ajay Arunachalam

metaEnsembleR_0.1.0.tar.gz
metaEnsembleR_0.1.0.zip(r-4.7-any)metaEnsembleR_0.1.0.zip(r-4.6-any)metaEnsembleR_0.1.0.zip(r-4.5-any)
metaEnsembleR_0.1.0.tgz(r-4.6-any)metaEnsembleR_0.1.0.tgz(r-4.5-any)
metaEnsembleR_0.1.0.tar.gz(r-4.7-any)metaEnsembleR_0.1.0.tar.gz(r-4.6-any)
metaEnsembleR_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
metaEnsembleR/json (API)

# Install 'metaEnsembleR' in R:

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

Bug tracker:https://github.com/ajayarunachalam/metaensembler/issues

On CRAN:

automation classification ensemble-machine-learning ensemble-methods generalization-error heterogenous meta-learning predictive-modeling regression stacked-ensembles

3.70 score 275 downloads 2 exports 76 dependencies

Last updated from:4379745243. Checks:7 WARNING, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel	WARNING	133
source / vignettes	OK	208
linux-release	WARNING	126
macos-release	WARNING	171
macos-oldrel	WARNING	261
windows-devel	WARNING	95
windows-release	WARNING	62
windows-oldrel	WARNING	81
wasm-release	OK	96

Exports:ensembler.classifier ensembler.regression

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

Intuitive Package for Meta-Ensemble Learning (Classification, Regression) that is Fully-Automated

Rendered frommetaEnsembleR.pdf.asisusingR.rsp::asis

Last update: 2020-10-28
Started: 2020-10-28

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Ensemble Classifiers Training & Prediction, Model Result Evaluation	ensembler.classifier
Ensemble Regressor Training & Prediction, Model Result Evaluation	ensembler.regression