MlBayesOpt: Hyper Parameter Tuning for Machine Learning, Using Bayesian Optimization

Hyper parameter tuning using Bayesian optimization (Shahriari et al. <doi:10.1109/JPROC.2015.2494218>) for support vector machine, random forest, and extreme gradient boosting (Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>). Unlike already existing packages (e.g. 'mlr', 'rBayesianOptimization', or 'xgboost'), there is no need to change in accordance with the package or method of machine learning. You just prepare a data frame with feature vectors and the label column that has any class ('character', 'factor', 'integer'). Moreover, to write a optimization function, you have only to specify the data and the column name of the label to classify.

Version: 0.3.4
Depends: R (≥ 3.1.0)
Imports: xgboost (≥ 0.6-4), Matrix, rBayesianOptimization (≥ 1.1.0), e1071 (≥ 1.6-8), ranger (≥ 0.8.0), data.table (≥ 1.9.6), foreach, rlang (≥ 0.1.2), dplyr (≥ 0.7.0)
Suggests: MASS, testthat, knitr, rmarkdown
Published: 2019-03-20
Author: Yuya Matsumura [aut, cre]
Maintainer: Yuya Matsumura <mattu.yuya at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README NEWS
CRAN checks: MlBayesOpt results


Reference manual: MlBayesOpt.pdf
Vignettes: R package to tune parameters for machine learning, using bayesian optimization with gaussian process
Package source: MlBayesOpt_0.3.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: MlBayesOpt_0.3.4.tgz, r-oldrel: MlBayesOpt_0.3.4.tgz
Old sources: MlBayesOpt archive


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