% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lgb.cv.R, R/lgb.train.R, R/lightgbm.R
\name{lgb.cv}
\alias{lgb.cv}
\alias{lgb.train}
\alias{lightgbm}
\title{Main CV logic for LightGBM}
\usage{
lgb.cv(params = list(), data, nrounds = 10, nfold = 3, label = NULL,
  weight = NULL, obj = NULL, eval = NULL, verbose = 1, record = TRUE,
  eval_freq = 1L, showsd = TRUE, stratified = TRUE, folds = NULL,
  init_model = NULL, colnames = NULL, categorical_feature = NULL,
  early_stopping_rounds = NULL, callbacks = list(), ...)

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,
  ...)

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{
\item{params}{List of parameters}

\item{data}{a \code{lgb.Dataset} object, used for CV}

\item{nrounds}{number of CV rounds}

\item{nfold}{the original dataset is randomly partitioned into \code{nfold} equal size subsamples.}

\item{label}{vector of response values. Should be provided only when data is an R-matrix.}

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

\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, can be (list of) character or custom eval function}

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

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

\item{eval_freq}{evalutaion output frequence, only effect when verbose > 0}

\item{showsd}{\code{boolean}, whether to show standard deviation of cross validation}

\item{stratified}{a \code{boolean} indicating whether sampling of folds should be stratified
by the values of outcome labels.}

\item{folds}{\code{list} provides a possibility to use a list of pre-defined CV folds
(each element must be a vector of test fold's indices). When folds are supplied,
the \code{nfold} and \code{stratified} parameters are ignored.}

\item{init_model}{path of model file of \code{lgb.Booster} object, will continue train 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
List of callback functions that are applied at each iteration.}

\item{...}{other parameters, see Parameters.rst for more informations}

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

\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{boosting}{boosting type. \code{gbdt}, \code{dart}}

\item{num_leaves}{number of leaves in one tree. defaults to 127}

\item{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{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).}

\item{params}{List of parameters}

\item{data}{a \code{lgb.Dataset} object, used for training}

\item{nrounds}{number of training rounds}

\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{boosting}{boosting type. \code{gbdt}, \code{dart}}

\item{num_leaves}{number of leaves in one tree. defaults to 127}

\item{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{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).}

\item{eval}{evaluation function, can be (a list of) character or custom eval function}

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

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

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

\item{...}{other parameters, see Parameters.rst for more informations}
}
\value{
a trained model \code{lgb.CVBooster}.

a trained booster model \code{lgb.Booster}.
}
\description{
Simple interface for training an lightgbm model.
Its documentation is combined with lgb.train.
}
\examples{
\dontrun{
library(lightgbm)
data(agaricus.train, package = "lightgbm")
train <- agaricus.train
dtrain <- lgb.Dataset(train$data, label = train$label)
params <- list(objective = "regression", metric = "l2")
model <- lgb.cv(params,
                dtrain,
                10,
                nfold = 5,
                min_data = 1,
                learning_rate = 1,
                early_stopping_rounds = 10)
}
\dontrun{
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)
}

}
