List of callback functions that are applied at each iteration.

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.

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.

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.

evaluation output frequency, only effect when verbose > 0

path of model file of lgb.Booster object, will continue training from this model

number of training rounds

objective function, can be character or custom objective function. Examples include regression, regression_l1, huber, binary, lambdarank, multiclass, multiclass

List of parameters

verbosity for output, if <= 0, also will disable the print of evaluation during training