Package: MlBayesOpt
Type: Package
Title: Hyper Parameter Tuning for Machine Learning, Using Bayesian
        Optimization
Version: 0.3.4
Authors@R: person("Yuya", "Matsumura", email = "mattu.yuya@gmail.com",
                  role = c("aut", "cre"))
Maintainer: Yuya Matsumura <mattu.yuya@gmail.com>
Description: 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.
Depends: R (>= 3.1.0)
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
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
RoxygenNote: 6.0.1
URL: https://github.com/ymattu/MlBayesOpt
BugReports: https://github.com/ymattu/MlBayesOpt/issues
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-03-20 07:04:30 UTC; matsumura
Author: Yuya Matsumura [aut, cre]
Repository: CRAN
Date/Publication: 2019-03-20 09:13:24 UTC
Built: R 3.4.4; ; 2019-04-25 22:13:43 UTC; windows
