Package: mllrnrs 0.0.8

mllrnrs: R6-Based ML Learners for 'mlexperiments'

Enhances 'mlexperiments' <https://CRAN.R-project.org/package=mlexperiments> with additional machine learning ('ML') learners. The package provides R6-based learners for the following algorithms: 'glmnet' <https://CRAN.R-project.org/package=glmnet>, 'ranger' <https://CRAN.R-project.org/package=ranger>, 'xgboost' <https://CRAN.R-project.org/package=xgboost>, and 'lightgbm' <https://CRAN.R-project.org/package=lightgbm>. These can be used directly with the 'mlexperiments' R package.

Authors:Lorenz A. Kapsner [cre, aut, cph]

mllrnrs_0.0.8.tar.gz
mllrnrs_0.0.8.zip(r-4.7)mllrnrs_0.0.8.zip(r-4.6)mllrnrs_0.0.8.zip(r-4.5)
mllrnrs_0.0.8.tgz(r-4.6-any)mllrnrs_0.0.8.tgz(r-4.5-any)
mllrnrs_0.0.8.tar.gz(r-4.7-any)mllrnrs_0.0.8.tar.gz(r-4.6-any)
mllrnrs_0.0.8.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
mllrnrs/json (API)

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

Bug tracker:https://github.com/kapsner/mllrnrs/issues

On CRAN:

Conda:

algorithmsexperimentsglmnetlearnerlightgbmmachine-learningrangerxgboostquarto

6.98 score 2 stars 1 packages 38 scripts 226 downloads 4 exports 67 dependencies

Last updated from:fee2c9d9eb. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK294
source / vignettesOK259
linux-release-x86_64OK307
macos-release-arm64OK234
macos-oldrel-arm64OK267
windows-develOK346
windows-releaseOK307
windows-oldrelOK311
wasm-releaseOK127

Exports:LearnerGlmnetLearnerLightgbmLearnerRangerLearnerXgboost

Dependencies:backportsbase64encbslibcachemcheckmatecliclustercodetoolscolorspacecpp11crayondata.tabledigestdoParallelevaluatefarverfastmapfontawesomeforeachforeignFormulafsggplot2gluegridExtragtablehighrHmischmshtmlTablehtmltoolshtmlwidgetsisobanditeratorsjquerylibjsonlitekdryknitrlabelinglifecyclemagrittrmemoisemimemlexperimentsnnetpkgconfigprettyunitsprogressR6rappdirsRColorBrewerrlangrmarkdownrpartrstudioapiS7sassscalessplitToolsstringistringrtinytexvctrsviridisLitewithrxfunyaml

glmnet: Binary Classification
Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Holdout Test Dataset Performance | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset
Rendered frommllrnrs_glmnet_binary.qmdusingquarto::html

Last update: 2026-01-15
Started: 2024-05-29

glmnet: Multiclass Classification
Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Holdout Test Dataset Performance | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset | Appendix I: Grid-Search with Target Weigths | Appendix II: k-Fold Cross Validation with Target Weigths
Rendered frommllrnrs_glmnet_multiclass.qmdusingquarto::html

Last update: 2026-01-15
Started: 2024-05-29

glmnet: Regression
Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Holdout Test Dataset Performance | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset
Rendered frommllrnrs_glmnet_regression.qmdusingquarto::html

Last update: 2026-01-15
Started: 2024-05-29

lightgbm: Binary Classification
Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Holdout Test Dataset Performance | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset
Rendered frommllrnrs_lightgbm_binary.qmdusingquarto::html

Last update: 2026-01-15
Started: 2024-05-29

lightgbm: Multiclass Classification
Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Holdout Test Dataset Performance | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset | Appendix I: Grid-Search with Target Weigths | Appendix II: k-Fold Cross Validation with Target Weigths
Rendered frommllrnrs_lightgbm_multiclass.qmdusingquarto::html

Last update: 2026-01-15
Started: 2024-05-29

lightgbm: Regression
Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Holdout Test Dataset Performance | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset
Rendered frommllrnrs_lightgbm_regression.qmdusingquarto::html

Last update: 2026-01-15
Started: 2024-05-29

ranger: Binary Classification
Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Holdout Test Dataset Performance | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset
Rendered frommllrnrs_ranger_binary.qmdusingquarto::html

Last update: 2026-01-15
Started: 2024-05-29

ranger: Multiclass Classification
Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Holdout Test Dataset Performance | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset | Appendix I: Grid-Search with Target Weigths | Appendix II: k-Fold Cross Validation with Target Weigths
Rendered frommllrnrs_ranger_multiclass.qmdusingquarto::html

Last update: 2026-01-15
Started: 2024-05-29

ranger: Regression
Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Holdout Test Dataset Performance | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset
Rendered frommllrnrs_ranger_regression.qmdusingquarto::html

Last update: 2026-01-15
Started: 2024-05-29

xgboost: Binary Classification
Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Holdout Test Dataset Performance | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset
Rendered frommllrnrs_xgboost_binary.qmdusingquarto::html

Last update: 2026-01-15
Started: 2024-05-29

xgboost: Multiclass Classification
Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Holdout Test Dataset Performance | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset | Appendix I: Grid-Search with Target Weigths | Appendix II: k-Fold Cross Validation with Target Weigths
Rendered frommllrnrs_xgboost_multiclass.qmdusingquarto::html

Last update: 2026-01-15
Started: 2024-05-29

xgboost: Regression
Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Holdout Test Dataset Performance | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset
Rendered frommllrnrs_xgboost_regression.qmdusingquarto::html

Last update: 2026-01-15
Started: 2024-05-29