Simple interface for training a LightGBM model.

lightgbm(
  data,
  label = NULL,
  weight = NULL,
  params = list(),
  nrounds = 10L,
  verbose = 1L,
  eval_freq = 1L,
  early_stopping_rounds = NULL,
  save_name = "lightgbm.model",
  init_model = NULL,
  callbacks = list(),
  ...
)

Arguments

data

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.

label

Vector of labels, used if data is not an lgb.Dataset

weight

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

params

List of parameters

nrounds

number of training rounds

verbose

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

eval_freq

evaluation output frequency, only effect when verbose > 0

early_stopping_rounds

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.

save_name

File name to use when writing the trained model to disk. Should end in ".model".

init_model

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

callbacks

List of callback functions that are applied at each iteration.

...

Additional arguments passed to lgb.train. For example

  • valids: a list of lgb.Dataset objects, used for validation

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

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

  • record: Boolean, TRUE will record iteration message to booster$record_evals

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

  • categorical_feature: categorical features. This can either be a character vector of feature names or an integer vector with the indices of the features (e.g. c(1L, 10L) to say "the first and tenth columns").

  • reset_data: 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

  • 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).

Value

a trained lgb.Booster

Early Stopping

"early stopping" refers to stopping the training process if the model's performance on a given validation set does not improve for several consecutive iterations.

If multiple arguments are given to eval, their order will be preserved. If you enable early stopping by setting early_stopping_rounds in params, by default all metrics will be considered for early stopping.

If you want to only consider the first metric for early stopping, pass first_metric_only = TRUE in params. Note that if you also specify metric in params, that metric will be considered the "first" one. If you omit metric, a default metric will be used based on your choice for the parameter obj (keyword argument) or objective (passed into params).