% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lightgbm.R
\name{lightgbm}
\alias{lightgbm}
\title{Train a LightGBM model}
\usage{
lightgbm(
  data,
  label = NULL,
  weight = NULL,
  params = list(),
  nrounds = 10L,
  verbose = 1L,
  eval_freq = 1L,
  early_stopping_rounds = NULL,
  save_name = "lightgbm.model",
  init_model = NULL,
  callbacks = list(),
  ...
)
}
\arguments{
\item{data}{a \code{lgb.Dataset} object, used for training. Some functions, such as \code{\link{lgb.cv}},
may allow you to pass other types of data like \code{matrix} and then separately supply
\code{label} as a keyword argument.}

\item{label}{Vector of labels, used if \code{data} is not an \code{\link{lgb.Dataset}}}

\item{weight}{vector of response values. If not NULL, will set to dataset}

\item{params}{List of parameters}

\item{nrounds}{number of training rounds}

\item{verbose}{verbosity for output, if <= 0, also will disable the print of evaluation during training}

\item{eval_freq}{evaluation output frequency, only effect when verbose > 0}

\item{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.}

\item{save_name}{File name to use when writing the trained model to disk. Should end in ".model".}

\item{init_model}{path of model file of \code{lgb.Booster} object, will continue training from this model}

\item{callbacks}{List of callback functions that are applied at each iteration.}

\item{...}{Additional arguments passed to \code{\link{lgb.train}}. For example
\itemize{
   \item{\code{valids}: a list of \code{lgb.Dataset} objects, used for validation}
   \item{\code{obj}: objective function, can be character or custom objective function. Examples include
              \code{regression}, \code{regression_l1}, \code{huber},
               \code{binary}, \code{lambdarank}, \code{multiclass}, \code{multiclass}}
   \item{\code{eval}: evaluation function, can be (a list of) character or custom eval function}
   \item{\code{record}: Boolean, TRUE will record iteration message to \code{booster$record_evals}}
   \item{\code{colnames}: feature names, if not null, will use this to overwrite the names in dataset}
   \item{\code{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. \code{c(1L, 10L)} to
                       say "the first and tenth columns").}
   \item{\code{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}
    \item{\code{boosting}: Boosting type. \code{"gbdt"}, \code{"rf"}, \code{"dart"} or \code{"goss"}.}
    \item{\code{num_leaves}: Maximum number of leaves in one tree.}
    \item{\code{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.}
     \item{\code{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).}
}}
}
\description{
Simple interface for training a LightGBM model.
}
