Cross validation logic used by LightGBM

lgb.cv(
  params = list(),
  data,
  nrounds = 10L,
  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

List of parameters

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

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. 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.

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, not the number of threads (most CPU using hyper-threading to generate 2 threads per CPU core).

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

# \donttest{ data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) params <- list(objective = "regression", metric = "l2") model <- lgb.cv( params = params , data = dtrain , nrounds = 5L , nfold = 3L , min_data = 1L , learning_rate = 1.0 )
#> [LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000623 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] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000680 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] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000722 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 0.477199 #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf #> [LightGBM] [Info] Start training from score 0.488715 #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf #> [LightGBM] [Info] Start training from score 0.480424 #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf #> [1] "[1]: valid's l2:0.000460617+0.000651411" #> [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 #> [1] "[2]: valid's l2:0.000460617+0.000651411" #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf #> [1] "[3]: valid's l2:0.000460617+0.000651411" #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf #> [1] "[4]: valid's l2:0.000460617+0.000651411" #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf #> [1] "[5]: valid's l2:0.000460617+0.000651411"
# }