Description Usage Arguments Value Examples
This function estimates parameters for xgboost based on bayesian optimization.
1 2 3 4 5 6  xgb_opt(train_data, train_label, test_data, test_label, objectfun, evalmetric,
eta_range = c(0.1, 1L), max_depth_range = c(4L, 6L),
nrounds_range = c(70, 160L), subsample_range = c(0.1, 1L),
bytree_range = c(0.4, 1L), init_points = 4, n_iter = 10, acq = "ei",
kappa = 2.576, eps = 0, optkernel = list(type = "exponential", power =
2), classes = NULL)

train_data 
A data frame for training of xgboost 
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 
objectfun 
Specify the learning task and the corresponding learning objective

evalmetric 
evaluation metrics for validation data. Users can pass a selfdefined function to it. Default: metric will be assigned according to objective(rmse for regression, and error for classification, mean average precision for ranking).

eta_range 
The range of eta 
max_depth_range 
The range of max_depth 
nrounds_range 
The range of nrounds 
subsample_range 
The range of subsample rate 
bytree_range 
The range of colsample_bytree rate 
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 
classes 
set the number of classes. To use only with multiclass objectives. 
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 12 13 14 15  ## Not run:
library(MlBayesOpt)
set.seed(71)
res0 < xgb_opt(train_data = fashion_train,
train_label = y,
test_data = fashion_test,
test_label = y,
objectfun = "multi:softmax",
evalmetric = "merror",
classes = 10,
init_points = 3,
n_iter = 1)
## End(Not run)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.