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(), ... )
Vector of labels, used if
vector of response values. If not NULL, will set to dataset
List of parameters
number of training rounds
verbosity for output, if <= 0, also will disable the print of evaluation during training
evaluation output frequency, only effect when verbose > 0
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.
File name to use when writing the trained model to disk. Should end in ".model".
path of model file of
List of callback functions that are applied at each iteration.
Additional arguments passed to
"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
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
params, that metric will be considered the "first" one. If you omit
a default metric will be used based on your choice for the parameter
obj (keyword argument)
objective (passed into