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(),
...
)
a trained lgb.Booster
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.
Vector of labels, used if data
is not an lgb.Dataset
vector of response values. If not NULL, will set to dataset
a list of parameters. See the "Parameters" section of the documentation for a list of parameters and valid values.
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. 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.
File name to use when writing the trained model to disk. Should end in ".model".
path of model file of lgb.Booster
object, will continue training from this model
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).
"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
).