xgboost (version

xgb.train: eXtreme Gradient Boosting Training


xgb.train is an advanced interface for training an xgboost model. The xgboost function is a simpler wrapper for xgb.train.


  params = list(),
  watchlist = list(),
  obj = NULL,
  feval = NULL,
  verbose = 1,
  print_every_n = 1L,
  early_stopping_rounds = NULL,
  maximize = NULL,
  save_period = NULL,
  save_name = "xgboost.model",
  xgb_model = NULL,
  callbacks = list(),

xgboost( data = NULL, label = NULL, missing = NA, weight = NULL, params = list(), nrounds, verbose = 1, print_every_n = 1L, early_stopping_rounds = NULL, maximize = NULL, save_period = NULL, save_name = "xgboost.model", xgb_model = NULL, callbacks = list(), ... )



the list of parameters. The complete list of parameters is available in the online documentation. Below is a shorter summary:

1. General Parameters

  • booster which booster to use, can be gbtree or gblinear. Default: gbtree.

2. Booster Parameters

2.1. Parameter for Tree Booster

  • eta control the learning rate: scale the contribution of each tree by a factor of 0 < eta < 1 when it is added to the current approximation. Used to prevent overfitting by making the boosting process more conservative. Lower value for eta implies larger value for nrounds: low eta value means model more robust to overfitting but slower to compute. Default: 0.3

  • gamma minimum loss reduction required to make a further partition on a leaf node of the tree. the larger, the more conservative the algorithm will be.

  • max_depth maximum depth of a tree. Default: 6

  • min_child_weight minimum sum of instance weight (hessian) needed in a child. If the tree partition step results in a leaf node with the sum of instance weight less than min_child_weight, then the building process will give up further partitioning. In linear regression mode, this simply corresponds to minimum number of instances needed to be in each node. The larger, the more conservative the algorithm will be. Default: 1

  • subsample subsample ratio of the training instance. Setting it to 0.5 means that xgboost randomly collected half of the data instances to grow trees and this will prevent overfitting. It makes computation shorter (because less data to analyse). It is advised to use this parameter with eta and increase nrounds. Default: 1

  • colsample_bytree subsample ratio of columns when constructing each tree. Default: 1

  • num_parallel_tree Experimental parameter. number of trees to grow per round. Useful to test Random Forest through Xgboost (set colsample_bytree < 1, subsample < 1 and round = 1) accordingly. Default: 1

  • monotone_constraints A numerical vector consists of 1, 0 and -1 with its length equals to the number of features in the training data. 1 is increasing, -1 is decreasing and 0 is no constraint.

  • interaction_constraints A list of vectors specifying feature indices of permitted interactions. Each item of the list represents one permitted interaction where specified features are allowed to interact with each other. Feature index values should start from 0 (0 references the first column). Leave argument unspecified for no interaction constraints.

2.2. Parameter for Linear Booster

  • lambda L2 regularization term on weights. Default: 0

  • lambda_bias L2 regularization term on bias. Default: 0

  • alpha L1 regularization term on weights. (there is no L1 reg on bias because it is not important). Default: 0

3. Task Parameters

  • objective specify the learning task and the corresponding learning objective, users can pass a self-defined function to it. The default objective options are below:

    • reg:squarederror Regression with squared loss (Default).

    • reg:squaredlogerror: regression with squared log loss \(1/2 * (log(pred + 1) - log(label + 1))^2\). All inputs are required to be greater than -1. Also, see metric rmsle for possible issue with this objective.

    • reg:logistic logistic regression.

    • reg:pseudohubererror: regression with Pseudo Huber loss, a twice differentiable alternative to absolute loss.

    • binary:logistic logistic regression for binary classification. Output probability.

    • binary:logitraw logistic regression for binary classification, output score before logistic transformation.

    • binary:hinge: hinge loss for binary classification. This makes predictions of 0 or 1, rather than producing probabilities.

    • count:poisson: poisson regression for count data, output mean of poisson distribution. max_delta_step is set to 0.7 by default in poisson regression (used to safeguard optimization).

    • survival:cox: Cox regression for right censored survival time data (negative values are considered right censored). Note that predictions are returned on the hazard ratio scale (i.e., as HR = exp(marginal_prediction) in the proportional hazard function h(t) = h0(t) * HR).

    • survival:aft: Accelerated failure time model for censored survival time data. See Survival Analysis with Accelerated Failure Time for details.

    • aft_loss_distribution: Probabilty Density Function used by survival:aft and aft-nloglik metric.

    • multi:softmax set xgboost to do multiclass classification using the softmax objective. Class is represented by a number and should be from 0 to num_class - 1.

    • multi:softprob same as softmax, but prediction outputs a vector of ndata * nclass elements, which can be further reshaped to ndata, nclass matrix. The result contains predicted probabilities of each data point belonging to each class.

    • rank:pairwise set xgboost to do ranking task by minimizing the pairwise loss.

    • rank:ndcg: Use LambdaMART to perform list-wise ranking where Normalized Discounted Cumulative Gain (NDCG) is maximized.

    • rank:map: Use LambdaMART to perform list-wise ranking where Mean Average Precision (MAP) is maximized.

    • reg:gamma: gamma regression with log-link. Output is a mean of gamma distribution. It might be useful, e.g., for modeling insurance claims severity, or for any outcome that might be gamma-distributed.

    • reg:tweedie: Tweedie regression with log-link. It might be useful, e.g., for modeling total loss in insurance, or for any outcome that might be Tweedie-distributed.

  • base_score the initial prediction score of all instances, global bias. Default: 0.5

  • eval_metric 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). List is provided in detail section.


training dataset. xgb.train accepts only an xgb.DMatrix as the input. xgboost, in addition, also accepts matrix, dgCMatrix, or name of a local data file.


max number of boosting iterations.


named list of xgb.DMatrix datasets to use for evaluating model performance. Metrics specified in either eval_metric or feval will be computed for each of these datasets during each boosting iteration, and stored in the end as a field named evaluation_log in the resulting object. When either verbose>=1 or cb.print.evaluation callback is engaged, the performance results are continuously printed out during the training. E.g., specifying watchlist=list(validation1=mat1, validation2=mat2) allows to track the performance of each round's model on mat1 and mat2.


customized objective function. Returns gradient and second order gradient with given prediction and dtrain.


customized evaluation function. Returns list(metric='metric-name', value='metric-value') with given prediction and dtrain.


If 0, xgboost will stay silent. If 1, it will print information about performance. If 2, some additional information will be printed out. Note that setting verbose > 0 automatically engages the cb.print.evaluation(period=1) callback function.


Print each n-th iteration evaluation messages when verbose>0. Default is 1 which means all messages are printed. This parameter is passed to the cb.print.evaluation callback.


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. Setting this parameter engages the cb.early.stop callback.


If feval and early_stopping_rounds are set, then this parameter must be set as well. When it is TRUE, it means the larger the evaluation score the better. This parameter is passed to the cb.early.stop callback.


when it is non-NULL, model is saved to disk after every save_period rounds, 0 means save at the end. The saving is handled by the cb.save.model callback.


the name or path for periodically saved model file.


a previously built model to continue the training from. Could be either an object of class xgb.Booster, or its raw data, or the name of a file with a previously saved model.


a list of callback functions to perform various task during boosting. See callbacks. Some of the callbacks are automatically created depending on the parameters' values. User can provide either existing or their own callback methods in order to customize the training process.


other parameters to pass to params.


vector of response values. Should not be provided when data is a local data file name or an xgb.DMatrix.


by default is set to NA, which means that NA values should be considered as 'missing' by the algorithm. Sometimes, 0 or other extreme value might be used to represent missing values. This parameter is only used when input is a dense matrix.


a vector indicating the weight for each row of the input.


An object of class xgb.Booster with the following elements:

  • handle a handle (pointer) to the xgboost model in memory.

  • raw a cached memory dump of the xgboost model saved as R's raw type.

  • niter number of boosting iterations.

  • evaluation_log evaluation history stored as a data.table with the first column corresponding to iteration number and the rest corresponding to evaluation metrics' values. It is created by the cb.evaluation.log callback.

  • call a function call.

  • params parameters that were passed to the xgboost library. Note that it does not capture parameters changed by the cb.reset.parameters callback.

  • callbacks callback functions that were either automatically assigned or explicitly passed.

  • best_iteration iteration number with the best evaluation metric value (only available with early stopping).

  • best_ntreelimit the ntreelimit value corresponding to the best iteration, which could further be used in predict method (only available with early stopping).

  • best_score the best evaluation metric value during early stopping. (only available with early stopping).

  • feature_names names of the training dataset features (only when column names were defined in training data).

  • nfeatures number of features in training data.


These are the training functions for xgboost.

The xgb.train interface supports advanced features such as watchlist, customized objective and evaluation metric functions, therefore it is more flexible than the xgboost interface.

Parallelization is automatically enabled if OpenMP is present. Number of threads can also be manually specified via nthread parameter.

The evaluation metric is chosen automatically by Xgboost (according to the objective) when the eval_metric parameter is not provided. User may set one or several eval_metric parameters. Note that when using a customized metric, only this single metric can be used. The following is the list of built-in metrics for which Xgboost provides optimized implementation:

The following callbacks are automatically created when certain parameters are set:

  • cb.print.evaluation is turned on when verbose > 0; and the print_every_n parameter is passed to it.

  • cb.evaluation.log is on when watchlist is present.

  • cb.early.stop: when early_stopping_rounds is set.

  • cb.save.model: when save_period > 0 is set.


Tianqi Chen and Carlos Guestrin, "XGBoost: A Scalable Tree Boosting System", 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016, https://arxiv.org/abs/1603.02754

See Also

callbacks, predict.xgb.Booster, xgb.cv


data(agaricus.train, package='xgboost')
data(agaricus.test, package='xgboost')

dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
watchlist <- list(train = dtrain, eval = dtest)

## A simple xgb.train example:
param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = 2,
              objective = "binary:logistic", eval_metric = "auc")
bst <- xgb.train(param, dtrain, nrounds = 2, watchlist)

## An xgb.train example where custom objective and evaluation metric are used:
logregobj <- function(preds, dtrain) {
   labels <- getinfo(dtrain, "label")
   preds <- 1/(1 + exp(-preds))
   grad <- preds - labels
   hess <- preds * (1 - preds)
   return(list(grad = grad, hess = hess))
evalerror <- function(preds, dtrain) {
  labels <- getinfo(dtrain, "label")
  err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
  return(list(metric = "error", value = err))

# These functions could be used by passing them either:
#  as 'objective' and 'eval_metric' parameters in the params list:
param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = 2,
              objective = logregobj, eval_metric = evalerror)
bst <- xgb.train(param, dtrain, nrounds = 2, watchlist)

#  or through the ... arguments:
param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = 2)
bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
                 objective = logregobj, eval_metric = evalerror)

#  or as dedicated 'obj' and 'feval' parameters of xgb.train:
bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
                 obj = logregobj, feval = evalerror)

## An xgb.train example of using variable learning rates at each iteration:
param <- list(max_depth = 2, eta = 1, verbose = 0, nthread = 2,
              objective = "binary:logistic", eval_metric = "auc")
my_etas <- list(eta = c(0.5, 0.1))
bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
                 callbacks = list(cb.reset.parameters(my_etas)))

## Early stopping:
bst <- xgb.train(param, dtrain, nrounds = 25, watchlist,
                 early_stopping_rounds = 3)

## An 'xgboost' interface example:
bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label,
               max_depth = 2, eta = 1, nthread = 2, nrounds = 2,
               objective = "binary:logistic")
pred <- predict(bst, agaricus.test$data)

# }