lightgbm | R Documentation |
High-level R interface to train a LightGBM model. Unlike lgb.train
, this function
is focused on compatibility with other statistics and machine learning interfaces in R.
This focus on compatibility means that this interface may experience more frequent breaking API changes
than lgb.train
.
For efficiency-sensitive applications, or for applications where breaking API changes across releases
is very expensive, use lgb.train
.
lightgbm(
data,
label = NULL,
weights = NULL,
params = list(),
nrounds = 100L,
verbose = 1L,
eval_freq = 1L,
early_stopping_rounds = NULL,
init_model = NULL,
callbacks = list(),
serializable = TRUE,
objective = "auto",
init_score = NULL,
num_threads = NULL,
colnames = NULL,
categorical_feature = NULL,
...
)
data |
a |
label |
Vector of labels, used if |
weights |
Sample / observation weights for rows in the input data. If Changed from 'weight', in version 4.0.0 |
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 and |
eval_freq |
evaluation output frequency, only effective when verbose > 0 and |
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 |
init_model |
path of model file or |
callbacks |
List of callback functions that are applied at each iteration. |
serializable |
whether to make the resulting objects serializable through functions such as
|
objective |
Optimization objective (e.g. '"regression"', '"binary"', etc.). For a list of accepted objectives, see the "objective" item of the "Parameters" section of the documentation. If passing
New in version 4.0.0 |
init_score |
initial score is the base prediction lightgbm will boost from New in version 4.0.0 |
num_threads |
Number of parallel threads to use. For best speed, this should be set to the number of physical cores in the CPU - in a typical x86-64 machine, this corresponds to half the number of maximum threads. Be aware that using too many threads can result in speed degradation in smaller datasets (see the parameters documentation for more details). If passing zero, will use the default number of threads configured for OpenMP
(typically controlled through an environment variable If passing This parameter gets overriden by New in version 4.0.0 |
colnames |
Character vector of features. Only used if |
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.
|
... |
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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