iris_test

datasets

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.

Yuya Matsumura

MlBayesOpt

Hyper Parameter Tuning for Machine Learning, Using Bayesian
Optimization

iris_test function

<p>An object of class <code>data.frame</code> with 75 rows and 5 columns.</p>

iris_test: Even-numbered rows of iris data

Description

Usage

Arguments

Format