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
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metaEnsembleR.pdf |metaEnsembleR.html✨
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 137 downloads 2 exports 78 dependencies

Last updated 4 years agofrom:4379745243. Checks:OK: 1 WARNING: 6. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Oct 31 2024
R-4.5-win	WARNING	Oct 31 2024
R-4.5-linux	WARNING	Oct 31 2024
R-4.4-win	WARNING	Oct 31 2024
R-4.4-mac	WARNING	Oct 31 2024
R-4.3-win	WARNING	Oct 31 2024
R-4.3-mac	WARNING	Oct 31 2024

Exports:ensembler.classifier ensembler.regression

Dependencies:caret class cli clock codetools colorspace cpp11 data.table diagram digest dplyr e1071 fansi 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 mgcv ModelMetrics munsell nlme nnet numDeriv parallelly pillar pkgconfig plyr pROC prodlim progressr proxy purrr R6 randomForest RColorBrewer Rcpp recipes reshape2 rlang rpart scales shape 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::asison Oct 31 2024.

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

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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