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 self-defined 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/cross-validation 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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