Package: mllrnrs 0.0.5

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_0.0.5.tar.gz
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mllrnrs.pdf |mllrnrs.html✨
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-learningrangerxgboost

6.93 score 2 stars 1 packages 26 scripts 336 downloads 4 exports 76 dependencies

Last updated 6 days agofrom:0b24c76266. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 05 2025
R-4.5-winOKMar 05 2025
R-4.5-macOKMar 05 2025
R-4.5-linuxOKMar 05 2025
R-4.4-winOKMar 05 2025
R-4.4-macOKMar 05 2025
R-4.4-linuxOKMar 05 2025
R-4.3-winOKMar 05 2025
R-4.3-macOKMar 05 2025

Exports:LearnerGlmnetLearnerLightgbmLearnerRangerLearnerXgboost

Dependencies:backportsbase64encbslibcachemcheckmatecliclustercodetoolscolorspacecrayondata.tabledigestdoParallelevaluatefansifarverfastmapfontawesomeforeachforeignFormulafsggplot2gluegridExtragtablehighrHmischmshtmlTablehtmltoolshtmlwidgetsisobanditeratorsjquerylibjsonlitekdryknitrlabelinglatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemlexperimentsmunsellnlmennetpillarpkgconfigprettyunitsprogressR6rappdirsRColorBrewerrlangrmarkdownrpartrstudioapisassscalessplitToolsstringistringrtibbletinytexutf8vctrsviridisviridisLitewithrxfunyaml

glmnet: Binary Classification

Rendered frommllrnrs_glmnet_binary.qmdusingquarto::htmlon Mar 05 2025.

Last update: 2024-07-11
Started: 2024-05-29

glmnet: Multiclass Classification

Rendered frommllrnrs_glmnet_multiclass.qmdusingquarto::htmlon Mar 05 2025.

Last update: 2024-07-11
Started: 2024-05-29

glmnet: Regression

Rendered frommllrnrs_glmnet_regression.qmdusingquarto::htmlon Mar 05 2025.

Last update: 2024-07-11
Started: 2024-05-29

lightgbm: Binary Classification

Rendered frommllrnrs_lightgbm_binary.qmdusingquarto::htmlon Mar 05 2025.

Last update: 2024-07-11
Started: 2024-05-29

lightgbm: Multiclass Classification

Rendered frommllrnrs_lightgbm_multiclass.qmdusingquarto::htmlon Mar 05 2025.

Last update: 2024-07-11
Started: 2024-05-29

lightgbm: Regression

Rendered frommllrnrs_lightgbm_regression.qmdusingquarto::htmlon Mar 05 2025.

Last update: 2024-07-11
Started: 2024-05-29

ranger: Binary Classification

Rendered frommllrnrs_ranger_binary.qmdusingquarto::htmlon Mar 05 2025.

Last update: 2024-07-11
Started: 2024-05-29

ranger: Multiclass Classification

Rendered frommllrnrs_ranger_multiclass.qmdusingquarto::htmlon Mar 05 2025.

Last update: 2024-07-11
Started: 2024-05-29

ranger: Regression

Rendered frommllrnrs_ranger_regression.qmdusingquarto::htmlon Mar 05 2025.

Last update: 2024-07-11
Started: 2024-05-29

xgboost: Binary Classification

Rendered frommllrnrs_xgboost_binary.qmdusingquarto::htmlon Mar 05 2025.

Last update: 2024-07-11
Started: 2024-05-29

xgboost: Multiclass Classification

Rendered frommllrnrs_xgboost_multiclass.qmdusingquarto::htmlon Mar 05 2025.

Last update: 2024-07-11
Started: 2024-05-29

xgboost: Regression

Rendered frommllrnrs_xgboost_regression.qmdusingquarto::htmlon Mar 05 2025.

Last update: 2024-07-11
Started: 2024-05-29