R: Main training logic for LightGBM

lgb.train {lightgbm} | R Documentation

## Main training logic for LightGBM

### Description

Logic to train with LightGBM

### Usage

    lgb.train(params = list(), data, nrounds = 10, valids = list(),
      obj = NULL, eval = NULL, verbose = 1, record = TRUE, eval_freq = 1L,
      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
`nrounds` | number of training rounds
`valids` | a list of `lgb.Dataset` objects, used for validation
`obj` | objective function, can be character or custom objective function. Examples include `regression`, `regression_l1`, `huber`, `binary`, `lambdarank`, `multiclass`, `multiclass`
`eval` | evaluation function, can be (a list of) character or custom eval function
`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
`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` | list of str or int type int represents index, type str represents feature names
`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 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: 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).

### Value

a trained booster model `lgb.Booster`.

### Examples

    library(lightgbm)
    data(agaricus.train, package = "lightgbm")
    train <- agaricus.train
    dtrain <- lgb.Dataset(train$data, label = train$label)
    data(agaricus.test, package = "lightgbm")
    test <- agaricus.test
    dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
    params <- list(objective = "regression", metric = "l2")
    valids <- list(test = dtest)
    model <- lgb.train(params,
                       dtrain,
                       100,
                       valids,
                       min_data = 1,
                       learning_rate = 1,
                       early_stopping_rounds = 10)

[Package lightgbm version 2.2.2 Index]