Package: mlexperiments 0.0.4

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_0.0.4.tar.gz
mlexperiments_0.0.4.zip(r-4.5)mlexperiments_0.0.4.zip(r-4.4)mlexperiments_0.0.4.zip(r-4.3)
mlexperiments_0.0.4.tgz(r-4.4-any)mlexperiments_0.0.4.tgz(r-4.3-any)
mlexperiments_0.0.4.tar.gz(r-4.5-noble)mlexperiments_0.0.4.tar.gz(r-4.4-noble)
mlexperiments_0.0.4.tgz(r-4.4-emscripten)mlexperiments_0.0.4.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'))

Peer review:

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

On CRAN:

cross-validationexperimenthyperparameter-optimizationhyperparameter-tuningmachine-learningnested

7.55 score 5 stars 2 packages 49 scripts 369 downloads 14 exports 75 dependencies

Last updated 4 months agofrom:58872947c2. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 02 2024
R-4.5-winOKNov 02 2024
R-4.5-linuxOKNov 02 2024
R-4.4-winOKNov 02 2024
R-4.4-macOKNov 02 2024
R-4.3-winOKNov 02 2024
R-4.3-macOKNov 02 2024

Exports:handle_cat_varsLearnerGlmLearnerKnnLearnerLmLearnerRpartmetricmetric_types_helperMLCrossValidationMLLearnerBaseMLNestedCVMLTuneParametersperformancepredictionsvalidate_fold_equality

Dependencies:backportsbase64encbslibcachemcheckmatecliclustercodetoolscolorspacecrayondata.tabledigestdoParallelevaluatefansifarverfastmapfontawesomeforeachforeignFormulafsggplot2gluegridExtragtablehighrHmischmshtmlTablehtmltoolshtmlwidgetsisobanditeratorsjquerylibjsonlitekdryknitrlabelinglatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemunsellnlmennetpillarpkgconfigprettyunitsprogressR6rappdirsRColorBrewerrlangrmarkdownrpartrstudioapisassscalessplitToolsstringistringrtibbletinytexutf8vctrsviridisviridisLitewithrxfunyaml

KNN: Binary Classification

Rendered frommlexperiments_knn_binary.qmdusingquarto::htmlon Nov 02 2024.

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

KNN: Multiclass Classification

Rendered frommlexperiments_knn_multiclass.qmdusingquarto::htmlon Nov 02 2024.

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

Rendered frommlexperiments_starter.qmdusingquarto::htmlon Nov 02 2024.

Last update: 2024-05-29
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rpart: Binary Classification

Rendered frommlexperiments_rpart_binary.qmdusingquarto::htmlon Nov 02 2024.

Last update: 2024-05-29
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rpart: Multiclass Classification

Rendered frommlexperiments_rpart_multiclass.qmdusingquarto::htmlon Nov 02 2024.

Last update: 2024-05-29
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rpart: Regression

Rendered frommlexperiments_rpart_regression.qmdusingquarto::htmlon Nov 02 2024.

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