lgb.cv: Main CV logic for LightGBM

In lightgbm: Light Gradient Boosting Machine

Main CV logic for LightGBM

Description

Cross validation logic used by LightGBM

Usage

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,
  ...
)

Arguments

params	a list of parameters. See the "Parameters" section of the documentation for a list of parameters and valid values.
data	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.
nrounds	number of training rounds
nfold	the original dataset is randomly partitioned into nfold equal size subsamples.
label	Vector of labels, used if data is not an lgb.Dataset
weight	vector of response values. If not NULL, will set to dataset
obj	objective function, can be character or custom objective function. Examples include regression, regression_l1, huber, binary, lambdarank, multiclass, multiclass
eval	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.
verbose	verbosity for output, if <= 0, also will disable the print of evaluation during training
record	Boolean, TRUE will record iteration message to booster$record_evals
eval_freq	evaluation output frequency, only effect when verbose > 0
showsd	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.
stratified	a boolean indicating whether sampling of folds should be stratified by the values of outcome labels.
folds	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.
init_model	path of model file of lgb.Booster object, will continue training from this model
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").
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 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.
callbacks	List of callback functions that are applied at each iteration.
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
...	other parameters, see Parameters.rst for more information. A few key parameters: 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). NOTE: As of v3.3.0, use of ... is deprecated. Add parameters to params directly.

Value

a trained model lgb.CVBooster.

Early Stopping

"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).

Examples

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
)
model <- lgb.cv(
  params = params
  , data = dtrain
  , nrounds = 5L
  , nfold = 3L
)

lightgbm documentation built on Jan. 17, 2023, 1:13 a.m.