Simple interface for training a LightGBM model.
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(),
lgb.Dataset object, used for training. Some functions, such as
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
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
lgb.Booster object, will continue training from this model
list of callback functions
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
eval: evaluation function, can be (a list of) character or custom eval function
record: Boolean, TRUE will record iteration message to
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.
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).