lightgbm: Train a LightGBM model

In lightgbm: Light Gradient Boosting Machine

Train a LightGBM model

Description

Simple interface for training a LightGBM model.

Usage

lightgbm(
  data,
  label = NULL,
  weight = NULL,
  params = list(),
  nrounds = 100L,
  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	a list of parameters. See the "Parameters" section of the documentation for a list of parameters and valid values.
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. When this parameter is non-null, training will stop if the evaluation of any metric on any validation set fails to improve for early_stopping_rounds consecutive boosting rounds. If training stops early, the returned model will have attribute best_iter set to the iteration number of the best iteration.
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(parallel::detectCores(logical = FALSE)), 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).

lightgbm documentation built on Jan. 17, 2023, 1:13 a.m.