lightgbm | R Documentation |
Simple interface for training a LightGBM model.
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(), ... )
data |
a |
label |
Vector of labels, used if |
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 |
save_name |
File name to use when writing the trained model to disk. Should end in ".model". |
init_model |
path of model file of |
callbacks |
List of callback functions that are applied at each iteration. |
... |
Additional arguments passed to
|
a trained lgb.Booster
"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
).
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