xgb_params is the list of parameters to train a XGB model using in training_model.
xgb_params_search is for searching the optimal parameters of xgboost,if any parameters of params in xgb_params is more than one.
xgb_params(
nrounds = 1000,
params = list(max_depth = 6, eta = 0.01, gamma = 0, min_child_weight = 1, subsample =
1, colsample_bytree = 1, scale_pos_weight = 1),
early_stopping_rounds = 100,
method = "random_search",
iters = 10,
f_eval = "auc",
nfold = 1,
nthread = 2,
...
)xgb_params_search(
dat_train,
target,
dat_test = NULL,
x_list = NULL,
prop = 0.7,
occur_time = NULL,
method = "random_search",
iters = 10,
nrounds = 100,
early_stopping_rounds = 10,
params = list(max_depth = 6, eta = 0.01, gamma = 0, min_child_weight = 1, subsample =
1, colsample_bytree = 1, scale_pos_weight = 1),
f_eval = "auc",
nfold = 1,
nthread = 2,
...
)
Max number of boosting iterations.
List of contains parameters of xgboost. The complete list of parameters is available at: http://xgboost.readthedocs.io/en/latest/parameter.html
If NULL, the early stopping function is not triggered. If set to an integer k, training with a validation set will stop if the performance doesn't improve for k rounds.
Method of searching optimal parameters."random_search","grid_search","local_search" are available.
Number of iterations of "random_search" optimal parameters.
Custimized evaluation function,"ks" & "auc" are available.
Number of the cross validation of xgboost
Number of threads
Other parameters
A data.frame of train data. Default is NULL.
Name of target variable.
A data.frame of test data. Default is NULL.
Names of independent variables. Default is NULL.
Percentage of train-data after the partition. Default: 0.7.
The name of the variable that represents the time at which each observation takes place.Default is NULL.
A list of parameters.