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.">

mlexperiments: Machine Learning Experiments (original) (raw)

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.

Version: 0.0.4
Depends: R (≥ 3.6)
Imports: data.table, kdry, parallel, progress, R6, splitTools, stats
Suggests: class, datasets, lintr, mlbench, mlr3measures, ParBayesianOptimization, quarto, rpart, testthat (≥ 3.0.1)
Published: 2024-07-05
DOI: 10.32614/CRAN.package.mlexperiments
Author: Lorenz A. Kapsner ORCID iD [cre, aut, cph]
Maintainer: Lorenz A. Kapsner <lorenz.kapsner at gmail.com>
BugReports: https://github.com/kapsner/mlexperiments/issues
License: GPL (≥ 3)
URL: https://github.com/kapsner/mlexperiments
NeedsCompilation: no
SystemRequirements: Quarto command line tools (https://github.com/quarto-dev/quarto-cli).
CRAN checks: mlexperiments results

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