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' <>) 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: 0.0.2
Depends: R (≥ 2.10)
Imports: data.table, kdry, parallel, progress, R6, splitTools, stats
Suggests: class, datasets, ggpubr, knitr, lintr, mlbench, mlr3measures, ParBayesianOptimization, rpart, testthat (≥ 3.0.1)
Published: 2023-06-10
Author: Lorenz A. Kapsner ORCID iD [cre, aut, cph]
Maintainer: Lorenz A. Kapsner <lorenz.kapsner at>
License: GPL (≥ 3)
NeedsCompilation: no
CRAN checks: mlexperiments results


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


Package source: mlexperiments_0.0.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): mlexperiments_0.0.2.tgz, r-oldrel (arm64): mlexperiments_0.0.2.tgz, r-release (x86_64): mlexperiments_0.0.2.tgz, r-oldrel (x86_64): mlexperiments_0.0.2.tgz

Reverse dependencies:

Reverse imports: mllrnrs, mlsurvlrnrs


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