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.

Package details

AuthorYuya Matsumura [aut, cre]
MaintainerYuya Matsumura <[email protected]>
LicenseMIT + file LICENSE
Version0.3.3
URL https://github.com/ymattu/MlBayesOpt
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("MlBayesOpt")

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MlBayesOpt documentation built on Nov. 17, 2017, 4:40 a.m.