Description Usage Arguments Value Examples
Bayesian Optimization for XGboost (Cross Validation)
1 2 3 4 5 6  xgb_cv_opt(data, label, objectfun, evalmetric, n_folds, 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,
seed = 0)

data 
data 
label 
label for classification 
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).

n_folds 
K for cross Validation 
eta_range 
The range of eta(default is c(0.1, 1L)) 
max_depth_range 
The range of max_depth(default is c(4L, 6L)) 
nrounds_range 
The range of nrounds(default is c(70, 160L)) 
subsample_range 
The range of subsample rate(default is c(0.1, 1L)) 
bytree_range 
The range of colsample_bytree rate(default is c(0.4, 1L) 
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 
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. 
seed 
set seed.(default is 0) 
The score you specified in the evalmetric option 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 < xgb_cv_opt(data = iris,
label = Species,
objectfun = "multi:softmax",
evalmetric = "mlogloss",
n_folds = 3,
classes = 3,
init_points = 2,
n_iter = 1)

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