Cross validation logic used by LightGBM = list(), data, nrounds = 10L, nfold = 3L,
  label = NULL, weight = NULL, obj = NULL, eval = NULL,
  verbose = 1L, record = TRUE, eval_freq = 1L, shows = TRUE,
  stratified = TRUE, folds = NULL, init_model = NULL,
  colnames = NULL, categorical_feature = NULL,
  early_stopping_rounds = NULL, callbacks = list(),
  reset_data = FALSE, ...)



List of parameters


a lgb.Dataset object, used for training


number of training rounds


the original dataset is randomly partitioned into nfold equal size subsamples.


vector of response values. Should be provided only when data is an R-matrix.


vector of response values. If not NULL, will set to dataset


objective function, can be character or custom objective function. Examples include regression, regression_l1, huber, binary, lambdarank, multiclass, multiclass


evaluation function, can be (list of) character or custom eval function


verbosity for output, if <= 0, also will disable the print of evaluation during training


Boolean, TRUE will record iteration message to booster$record_evals


evaluation output frequency, only effect when verbose > 0


a boolean indicating whether sampling of folds should be stratified by the values of outcome labels.


list provides a possibility to use a list of pre-defined CV folds (each element must be a vector of test fold's indices). When folds are supplied, the nfold and stratified parameters are ignored.


path of model file of lgb.Booster object, will continue training from this model


feature names, if not null, will use this to overwrite the names in dataset


list of str or int type int represents index, type str represents feature names


int. Activates early stopping. Requires at least one validation data and one metric. If there's more than one, will check all of them except the training data. Returns the model with (best_iter + early_stopping_rounds). If early stopping occurs, the model will have 'best_iter' field.


List of callback functions that are applied at each iteration.


Boolean, setting it to TRUE (not the default value) will transform the booster model into a predictor model which frees up memory and the original datasets


other parameters, see Parameters.rst for more information. A few key parameters:

  • boostingBoosting type. "gbdt" or "dart"

  • num_leavesnumber of leaves in one tree. defaults to 127

  • max_depthLimit the max depth for tree model. This is used to deal with overfit when #data is small. Tree still grow by leaf-wise.

  • num_threadsNumber of threads for LightGBM. For the best speed, set this to the number of real CPU cores, not the number of threads (most CPU using hyper-threading to generate 2 threads per CPU core).


boolean, whether to show standard deviation of cross validation


a trained model lgb.CVBooster.


library(lightgbm) data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) params <- list(objective = "regression", metric = "l2") model <- params = params , data = dtrain , nrounds = 10L , nfold = 3L , min_data = 1L , learning_rate = 1.0 , early_stopping_rounds = 5L )
#> Error: object 'showsd' not found