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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: |
0.0.3 |
| 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: |
2024-03-07 |
| Author: |
Lorenz A. Kapsner 
[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 |
| CRAN checks: |
mlexperiments results |
Documentation:
Downloads:
Reverse dependencies:
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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