library(xgboost)
xgboost(
  data = as.matrix(mtcars), label = runif(nrow(mtcars)),
  max_depth = 2, eta = 1, nthread = 2, nrounds = 2,
  objective = "reg:squaredlogerror"
)
#> [1]  train-rmsle:0.161837 
#> [2]  train-rmsle:0.112548
#> ##### xgb.Booster
#> raw: 1.2 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 = "reg:squaredlogerror")
#> params (as set within xgb.train):
#>   max_depth = "2", eta = "1", nthread = "2", objective = "reg:squaredlogerror", silent = "1"
#> xgb.attributes:
#>   niter
#> callbacks:
#>   cb.print.evaluation(period = print_every_n)
#>   cb.evaluation.log()
#> # of features: 11 
#> niter: 2
#> nfeatures : 11 
#> evaluation_log:
#>  iter train_rmsle
#>     1    0.161837
#>     2    0.112548
xgboost(
  data = as.matrix(mtcars), label = runif(nrow(mtcars)),
  max_depth = 2, eta = 1, nthread = 2, nrounds = 2,
  objective = "reg:squarederror"
)
#> [1]  train-rmse:0.217743 
#> [2]  train-rmse:0.192517
#> ##### xgb.Booster
#> raw: 1.2 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 = "reg:squarederror")
#> params (as set within xgb.train):
#>   max_depth = "2", eta = "1", nthread = "2", objective = "reg:squarederror", silent = "1"
#> xgb.attributes:
#>   niter
#> callbacks:
#>   cb.print.evaluation(period = print_every_n)
#>   cb.evaluation.log()
#> # of features: 11 
#> niter: 2
#> nfeatures : 11 
#> evaluation_log:
#>  iter train_rmse
#>     1   0.217743
#>     2   0.192517