% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lgb.train.R
\name{lgb.train}
\alias{lgb.train}
\title{Main training logic for LightGBM}
\usage{
lgb.train(
  params = list(),
  data,
  nrounds = 10L,
  valids = list(),
  obj = NULL,
  eval = NULL,
  verbose = 1L,
  record = TRUE,
  eval_freq = 1L,
  init_model = NULL,
  colnames = NULL,
  categorical_feature = NULL,
  early_stopping_rounds = NULL,
  callbacks = list(),
  reset_data = FALSE,
  ...
)
}
\arguments{
\item{params}{List of parameters}

\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{nrounds}{number of training rounds}

\item{valids}{a list of \code{lgb.Dataset} objects, used for validation}

\item{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{eval}{evaluation function(s). This can be a character vector, function, or list with a mixture of
            strings and functions.

            \itemize{
                \item{\bold{a. character vector}:
                    If you provide a character vector to this argument, it should contain strings with valid
                    evaluation metrics.
                    See \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#metric}{
                    The "metric" section of the documentation}
                    for a list of valid metrics.
                }
                \item{\bold{b. function}:
                     You can provide a custom evaluation function. This
                     should accept the keyword arguments \code{preds} and \code{dtrain} and should return a named
                     list with three elements:
                     \itemize{
                         \item{\code{name}: A string with the name of the metric, used for printing
                             and storing results.
                         }
                         \item{\code{value}: A single number indicating the value of the metric for the
                             given predictions and true values
                         }
                         \item{
                             \code{higher_better}: A boolean indicating whether higher values indicate a better fit.
                             For example, this would be \code{FALSE} for metrics like MAE or RMSE.
                         }
                     }
                }
                \item{\bold{c. list}:
                    If a list is given, it should only contain character vectors and functions.
                    These should follow the requirements from the descriptions above.
                }
            }}

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

\item{record}{Boolean, TRUE will record iteration message to \code{booster$record_evals}}

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

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

\item{colnames}{feature names, if not null, will use this to overwrite the names in dataset}

\item{categorical_feature}{list of str or int
type int represents index,
type str represents feature names}

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

\item{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{...}{other parameters, see Parameters.rst for more information. A few key parameters:
\itemize{
    \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).}
}}
}
\value{
a trained booster model \code{lgb.Booster}.
}
\description{
Logic to train with LightGBM
}
\section{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 \code{eval}, their order will be preserved. If you enable
         early stopping by setting \code{early_stopping_rounds} in \code{params}, by default all
         metrics will be considered for early stopping.

         If you want to only consider the first metric for early stopping, pass
         \code{first_metric_only = TRUE} in \code{params}. Note that if you also specify \code{metric}
         in \code{params}, that metric will be considered the "first" one. If you omit \code{metric},
         a default metric will be used based on your choice for the parameter \code{obj} (keyword argument)
         or \code{objective} (passed into \code{params}).
}

\examples{
\donttest{
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 = params
  , data = dtrain
  , nrounds = 5L
  , valids = valids
  , min_data = 1L
  , learning_rate = 1.0
  , early_stopping_rounds = 3L
)
}
}
