R: Shared parameter docs

lgb_shared_params {lightgbm} | R Documentation

## Shared parameter docs

### Description

Parameter docs shared by `lgb.train`, `lgb.cv`, and `lightgbm`

### Arguments

`callbacks` | List of callback functions that are applied at each iteration.
`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.
`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.
`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.
`eval_freq` | evaluation output frequency, only effect when verbose > 0
`init_model` | path of model file of `lgb.Booster` object, will continue training from this model
`nrounds` | number of training rounds
`obj` | objective function, can be character or custom objective function. Examples include `regression`, `regression_l1`, `huber`, `binary`, `lambdarank`, `multiclass`, `multiclass`
`params` | a list of parameters. See the "Parameters" section of the documentation for a list of parameters and valid values.
`verbose` | verbosity for output, if <= 0, also will disable the print of evaluation during training

### 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`).

[Package lightgbm version 3.3.2 Index]