R: Train a LightGBM model

lightgbm {lightgbm} | R Documentation

## Train a LightGBM model

### Description

Simple interface for training an LightGBM model.

### Usage

    lightgbm(data, label = NULL, weight = NULL, params = list(),
      nrounds = 10, verbose = 1, eval_freq = 1L,
      early_stopping_rounds = NULL, save_name = "lightgbm.model",
      init_model = NULL, callbacks = list(), ...)

### Arguments

`data` | a `lgb.Dataset` object, used for training
`label` | Vector of labels, used if `data` is not an `lgb.Dataset`
`weight` | vector of response values. If not NULL, will set to dataset
`params` | List of parameters
`nrounds` | number of training rounds
`verbose` | verbosity for output, if <= 0, also will disable the print of evaluation during training
`eval_freq` | evaluation output frequency, only effect when verbose > 0
`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
`save_name` | File name to use when writing the trained model to disk. Should end in ".model".
`init_model` | path of model file of `lgb.Booster` object, will continue training from this model
`callbacks` | list of callback functions List of callback functions that are applied at each iteration.
`...` | Additional arguments passed to `lgb.train`. For example validsa list of `lgb.Dataset` objects, used for validation objobjective function, can be character or custom objective function. Examples include `regression`, `regression_l1`, `huber`, `binary`, `lambdarank`, `multiclass`, `multiclass` evalevaluation function, can be (a list of) character or custom eval function recordBoolean, TRUE will record iteration message to `booster$record_evals` colnamesfeature names, if not null, will use this to overwrite the names in dataset categorical_featurelist of str or int. type int represents index, type str represents feature names reset_dataBoolean, 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 boostingBoosting type. `"gbdt"` or `"dart"` num_leavesnumber of leaves in one tree. defaults to 127 max_depthLimit the max depth for tree model. This is used to deal with overfit when #data is small. Tree still grow by leaf-wise. num_threadsNumber 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).

[Package lightgbm version 2.2.2 Index]