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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.

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

Documentation:

Reference manual: mlexperiments.pdf
Vignettes: KNN: Binary Classification
KNN: Multiclass Classification
rpart: Binary Classification
rpart: Multiclass Classification
rpart: Regression
mlexperiments: Getting Started

Downloads:

Package source: mlexperiments_0.0.4.tar.gz
Windows binaries: r-devel: mlexperiments_0.0.4.zip, r-release: mlexperiments_0.0.4.zip, r-oldrel: mlexperiments_0.0.4.zip
macOS binaries: r-release (arm64): mlexperiments_0.0.4.tgz, r-oldrel (arm64): mlexperiments_0.0.4.tgz, r-release (x86_64): mlexperiments_0.0.4.tgz, r-oldrel (x86_64): mlexperiments_0.0.4.tgz
Old sources: mlexperiments archive

Reverse dependencies:

Reverse imports: mllrnrs, mlsurvlrnrs

Linking:

Please use the canonical form https://CRAN.R-project.org/package=mlexperiments to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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