Print xgb.Booster

Print information about xgb.Booster.

# S3 method for xgb.Booster
print(x, verbose = FALSE, ...)

Arguments

x	an xgb.Booster object
verbose	whether to print detailed data (e.g., attribute values)
...	not currently used

Examples

data(agaricus.train, package='xgboost')
train <- agaricus.train
bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
               eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
#> [1]	train-error:0.046522 
#> [2]	train-error:0.022263 
attr(bst, 'myattr') <- 'memo'

print(bst)
#> ##### xgb.Booster
#> raw: 1.1 Kb 
#> call:
#>   xgb.train(params = params, data = dtrain, nrounds = nrounds, 
#>     watchlist = watchlist, verbose = verbose, print_every_n = print_every_n, 
#>     early_stopping_rounds = early_stopping_rounds, maximize = maximize, 
#>     save_period = save_period, save_name = save_name, xgb_model = xgb_model, 
#>     callbacks = callbacks, max_depth = 2, eta = 1, nthread = 2, 
#>     objective = "binary:logistic")
#> params (as set within xgb.train):
#>   max_depth = "2", eta = "1", nthread = "2", objective = "binary:logistic", silent = "1"
#> xgb.attributes:
#>   niter
#> callbacks:
#>   cb.print.evaluation(period = print_every_n)
#>   cb.evaluation.log()
#> # of features: 126 
#> niter: 2
#> nfeatures : 126 
#> evaluation_log:
#>  iter train_error
#>     1    0.046522
#>     2    0.022263
print(bst, verbose=TRUE)
#> ##### xgb.Booster
#> raw: 1.1 Kb 
#> call:
#>   xgb.train(params = params, data = dtrain, nrounds = nrounds, 
#>     watchlist = watchlist, verbose = verbose, print_every_n = print_every_n, 
#>     early_stopping_rounds = early_stopping_rounds, maximize = maximize, 
#>     save_period = save_period, save_name = save_name, xgb_model = xgb_model, 
#>     callbacks = callbacks, max_depth = 2, eta = 1, nthread = 2, 
#>     objective = "binary:logistic")
#> params (as set within xgb.train):
#>   max_depth = "2", eta = "1", nthread = "2", objective = "binary:logistic", silent = "1"
#> xgb.attributes:
#>   niter = "1"
#> callbacks:
#>   cb.print.evaluation(period = print_every_n)
#>   cb.evaluation.log()
#> # of features: 126 
#> niter: 2
#> nfeatures : 126 
#> evaluation_log:
#>  iter train_error
#>     1    0.046522
#>     2    0.022263