Logic to train with LightGBM

lgb.train(params = list(), data, nrounds = 10L, valids = list(),
  obj = NULL, eval = NULL, verbose = 1L, record = TRUE,
  eval_freq = 1L, 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. Some functions, such as lgb.cv, may allow you to pass other types of data like matrix and then separately supply label as a keyword argument.


number of training rounds


a list of lgb.Dataset objects, used for validation


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


evaluation function, can be (a 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


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:

  • boosting: Boosting type. "gbdt", "rf", "dart" or "goss".

  • num_leaves: Maximum number of leaves in one tree.

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

  • num_threads: Number 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).


a trained booster model lgb.Booster.


library(lightgbm) data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) data(agaricus.test, package = "lightgbm") test <- agaricus.test dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) params <- list(objective = "regression", metric = "l2") valids <- list(test = dtest) model <- lgb.train( params = params , data = dtrain , nrounds = 10L , valids = valids , min_data = 1L , learning_rate = 1.0 , early_stopping_rounds = 5L )
#> [1]: test's l2:6.44165e-17 #> [2]: test's l2:6.44165e-17 #> [3]: test's l2:6.44165e-17 #> [4]: test's l2:6.44165e-17 #> [5]: test's l2:6.44165e-17 #> [6]: test's l2:6.44165e-17