Cross validation logic used by LightGBM
lgb.cv(
params = list(),
data,
nrounds = 100L,
nfold = 3L,
label = NULL,
weight = NULL,
obj = NULL,
eval = NULL,
verbose = 1L,
record = TRUE,
eval_freq = 1L,
showsd = TRUE,
stratified = TRUE,
folds = NULL,
init_model = NULL,
colnames = NULL,
categorical_feature = NULL,
early_stopping_rounds = NULL,
callbacks = list(),
reset_data = FALSE,
serializable = TRUE,
eval_train_metric = FALSE
)a list of parameters. See the "Parameters" section of the documentation for a list of parameters and valid values.
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.
number of training rounds
the original dataset is randomly partitioned into nfold equal size subsamples.
Deprecated. See "Deprecated Arguments" section below.
Deprecated. See "Deprecated Arguments" section below.
objective function, can be character or custom objective function. Examples include
regression, regression_l1, huber,
binary, lambdarank, multiclass, multiclass
evaluation function(s). This can be a character vector, function, or list with a mixture of strings and functions.
a. character vector: If you provide a character vector to this argument, it should contain strings with valid evaluation metrics. See The "metric" section of the documentation for a list of valid metrics.
b. function:
You can provide a custom evaluation function. This
should accept the keyword arguments preds and dtrain and should return a named
list with three elements:
name: A string with the name of the metric, used for printing
and storing results.
value: A single number indicating the value of the metric for the
given predictions and true values
higher_better: A boolean indicating whether higher values indicate a better fit.
For example, this would be FALSE for metrics like MAE or RMSE.
c. list: If a list is given, it should only contain character vectors and functions. These should follow the requirements from the descriptions above.
verbosity for output, if <= 0 and valids has been provided, also will disable the
printing of evaluation during training
Boolean, TRUE will record iteration message to booster$record_evals
evaluation output frequency, only effective when verbose > 0 and valids has been provided
boolean, whether to show standard deviation of cross validation.
This parameter defaults to TRUE. Setting it to FALSE can lead to a
slight speedup by avoiding unnecessary computation.
a boolean indicating whether sampling of folds should be stratified
by the values of outcome labels.
list provides a possibility to use a list of pre-defined CV folds
(each element must be a vector of test fold's indices). When folds are supplied,
the nfold and stratified parameters are ignored.
path of model file or lgb.Booster object, will continue training from this model
Deprecated. See "Deprecated Arguments" section below.
Deprecated. See "Deprecated Arguments" section below.
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.
List of callback functions that are applied at each iteration.
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
whether to make the resulting objects serializable through functions such as
save or saveRDS (see section "Model serialization").
boolean, whether to add the cross validation results on the
training data. This parameter defaults to FALSE. Setting it to TRUE
will increase run time.
a trained model lgb.CVBooster.
A future release of lightgbm will require passing an lgb.Dataset
to argument 'data'. It will also remove support for passing arguments
'categorical_feature', 'colnames', 'label', and 'weight'.
"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).
# \donttest{
setLGBMthreads(2L)
data.table::setDTthreads(1L)
data(agaricus.train, package = "lightgbm")
train <- agaricus.train
dtrain <- lgb.Dataset(train$data, label = train$label)
params <- list(
objective = "regression"
, metric = "l2"
, min_data = 1L
, learning_rate = 1.0
, num_threads = 2L
)
model <- lgb.cv(
params = params
, data = dtrain
, nrounds = 5L
, nfold = 3L
)
#> [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000634 seconds.
#> You can set `force_row_wise=true` to remove the overhead.
#> And if memory is not enough, you can set `force_col_wise=true`.
#> [LightGBM] [Info] Total Bins 232
#> [LightGBM] [Info] Number of data points in the train set: 4342, number of used features: 116
#> [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000605 seconds.
#> You can set `force_row_wise=true` to remove the overhead.
#> And if memory is not enough, you can set `force_col_wise=true`.
#> [LightGBM] [Info] Total Bins 232
#> [LightGBM] [Info] Number of data points in the train set: 4342, number of used features: 116
#> [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000597 seconds.
#> You can set `force_row_wise=true` to remove the overhead.
#> And if memory is not enough, you can set `force_col_wise=true`.
#> [LightGBM] [Info] Total Bins 232
#> [LightGBM] [Info] Number of data points in the train set: 4342, number of used features: 116
#> [LightGBM] [Info] Start training from score 0.474436
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [LightGBM] [Info] Start training from score 0.490557
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [LightGBM] [Info] Start training from score 0.481345
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [1]: valid's l2:0.000307078+0.000434274
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [2]: valid's l2:0.000307078+0.000434274
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [3]: valid's l2:0.000307078+0.000434274
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [4]: valid's l2:0.000307078+0.000434274
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [5]: valid's l2:0.000307078+0.000434274
# }