Description Usage Arguments Value Examples
This function estimates parameters for Random Forest based on bayesian optimization.
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train_data 
A data frame for training of Random Forest 
train_label 
The column of class to classify in the training data 
test_data 
A data frame for training of xgboost 
test_label 
The column of class to classify in the test data 
num_tree 
The range of the number of trees for forest. Defaults to 500 (no optimization). 
mtry_range 
Value of mtry used. Defaults from 1 to number of features. 
min_node_size_range 
The range of minimum node sizes to best tested. Default min is 1 and max is sqrt(nrow(train_data)). 
init_points 
Number of randomly chosen points to sample the target function before Bayesian Optimization fitting the Gaussian Process. 
n_iter 
Total number of times the Bayesian Optimization is to repeated. 
acq 
Acquisition function type to be used. Can be "ucb", "ei" or "poi".

kappa 
tunable parameter kappa of GP Upper Confidence Bound, to balance exploitation against exploration, increasing kappa will make the optimized hyperparameters pursuing exploration. 
eps 
tunable parameter epsilon of Expected Improvement and Probability of Improvement, to balance exploitation against exploration, increasing epsilon will make the optimized hyperparameters are more spread out across the whole range. 
optkernel 
Kernel (aka correlation function) for the underlying Gaussian Process. This parameter should be a list that specifies the type of correlation function along with the smoothness parameter. Popular choices are square exponential (default) or matern 5/2 
The test accuracy and a list of Bayesian Optimization result is returned:
Best_Par
a named vector of the best hyperparameter set found
Best_Value
the value of metrics achieved by the best hyperparameter set
History
a data.table
of the bayesian optimization history
Pred
a data.table
with validation/crossvalidation prediction for each round of bayesian optimization history
1 2 3 4 5 6 7 8 9 10 11  library(MlBayesOpt)
set.seed(71)
res0 < rf_opt(train_data = iris_train,
train_label = Species,
test_data = iris_test,
test_label = Species,
mtry_range = c(1L, ncol(iris_train)  1),
num_tree = 10L,
init_points = 10,
n_iter = 1)

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