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.
|Author||Yuya Matsumura [aut, cre]|
|Maintainer||Yuya Matsumura <[email protected]>|
|License||MIT + file LICENSE|
|Package repository||View on CRAN|
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