Package: mlexperiments 0.0.5

mlexperiments: Machine Learning Experiments

Provides 'R6' objects to perform parallelized hyperparameter optimization and cross-validation. Hyperparameter optimization can be performed with Bayesian optimization (via 'ParBayesianOptimization' <https://cran.r-project.org/package=ParBayesianOptimization>) and grid search. The optimized hyperparameters can be validated using k-fold cross-validation. Alternatively, hyperparameter optimization and validation can be performed with nested cross-validation. While 'mlexperiments' focuses on core wrappers for machine learning experiments, additional learner algorithms can be supplemented by inheriting from the provided learner base class.

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

mlexperiments_0.0.5.tar.gz
mlexperiments_0.0.5.zip(r-4.5)mlexperiments_0.0.5.zip(r-4.4)mlexperiments_0.0.5.zip(r-4.3)
mlexperiments_0.0.5.tgz(r-4.5-any)mlexperiments_0.0.5.tgz(r-4.4-any)mlexperiments_0.0.5.tgz(r-4.3-any)
mlexperiments_0.0.5.tar.gz(r-4.5-noble)mlexperiments_0.0.5.tar.gz(r-4.4-noble)
mlexperiments_0.0.5.tgz(r-4.4-emscripten)mlexperiments_0.0.5.tgz(r-4.3-emscripten)
mlexperiments.pdf |mlexperiments.html
mlexperiments/json (API)

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

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

On CRAN:

Conda:

cross-validationexperimenthyperparameter-optimizationhyperparameter-tuningmachine-learningnested

7.64 score 5 stars 2 packages 49 scripts 401 downloads 14 exports 75 dependencies

Last updated 20 days agofrom:335d205bf4. 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:handle_cat_varsLearnerGlmLearnerKnnLearnerLmLearnerRpartmetricmetric_types_helperMLCrossValidationMLLearnerBaseMLNestedCVMLTuneParametersperformancepredictionsvalidate_fold_equality

Dependencies:backportsbase64encbslibcachemcheckmatecliclustercodetoolscolorspacecrayondata.tabledigestdoParallelevaluatefansifarverfastmapfontawesomeforeachforeignFormulafsggplot2gluegridExtragtablehighrHmischmshtmlTablehtmltoolshtmlwidgetsisobanditeratorsjquerylibjsonlitekdryknitrlabelinglatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemunsellnlmennetpillarpkgconfigprettyunitsprogressR6rappdirsRColorBrewerrlangrmarkdownrpartrstudioapisassscalessplitToolsstringistringrtibbletinytexutf8vctrsviridisviridisLitewithrxfunyaml

KNN: Binary Classification

Rendered frommlexperiments_knn_binary.qmdusingquarto::htmlon Mar 05 2025.

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

KNN: Multiclass Classification

Rendered frommlexperiments_knn_multiclass.qmdusingquarto::htmlon Mar 05 2025.

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

mlexperiments: Getting Started

Rendered frommlexperiments_starter.qmdusingquarto::htmlon Mar 05 2025.

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

rpart: Binary Classification

Rendered frommlexperiments_rpart_binary.qmdusingquarto::htmlon Mar 05 2025.

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

rpart: Multiclass Classification

Rendered frommlexperiments_rpart_multiclass.qmdusingquarto::htmlon Mar 05 2025.

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

rpart: Regression

Rendered frommlexperiments_rpart_regression.qmdusingquarto::htmlon Mar 05 2025.

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