[docs] R docs cleanup (#2375)

* R docs cleanup

* regenerated R documentation files

R-package/R/lgb.Dataset.R

@@ -635,9 +635,9 @@ Dataset <- R6::R6Class(
   )
 )
 
-#' Construct lgb.Dataset object
+#' Construct \code{lgb.Dataset} object
 #'
-#' Construct lgb.Dataset object from dense matrix, sparse matrix
+#' Construct \code{lgb.Dataset} object from dense matrix, sparse matrix
 #' or local file (that was created previously by saving an \code{lgb.Dataset}).
 #'
 #' @param data a \code{matrix} object, a \code{dgCMatrix} object or a character representing a filename
@@ -646,7 +646,7 @@ Dataset <- R6::R6Class(
 #' @param colnames names of columns
 #' @param categorical_feature categorical features
 #' @param free_raw_data TRUE for need to free raw data after construct
-#' @param info a list of information of the lgb.Dataset object
+#' @param info a list of information of the \code{lgb.Dataset} object
 #' @param ... other information to pass to \code{info} or parameters pass to \code{params}
 #'
 #' @return constructed dataset
@@ -690,7 +690,7 @@ lgb.Dataset <- function(data,
 #'
 #' @param dataset \code{lgb.Dataset} object, training data
 #' @param data a \code{matrix} object, a \code{dgCMatrix} object or a character representing a filename
-#' @param info a list of information of the lgb.Dataset object
+#' @param info a list of information of the \code{lgb.Dataset} object
 #' @param ... other information to pass to \code{info}.
 #'
 #' @return constructed dataset
@@ -741,7 +741,7 @@ lgb.Dataset.construct <- function(dataset) {
 
 }
 
-#' Dimensions of an lgb.Dataset
+#' Dimensions of an \code{lgb.Dataset}
 #'
 #' Returns a vector of numbers of rows and of columns in an \code{lgb.Dataset}.
 #' @param x Object of class \code{lgb.Dataset}
@@ -852,9 +852,9 @@ dimnames.lgb.Dataset <- function(x) {
 #' Slice a dataset
 #'
 #' Get a new \code{lgb.Dataset} containing the specified rows of
-#' original lgb.Dataset object
+#' original \code{lgb.Dataset} object
 #'
-#' @param dataset Object of class "lgb.Dataset"
+#' @param dataset Object of class \code{lgb.Dataset}
 #' @param idxset a integer vector of indices of rows needed
 #' @param ... other parameters (currently not used)
 #' @return constructed sub dataset
@@ -888,7 +888,7 @@ slice.lgb.Dataset <- function(dataset, idxset, ...) {
 
 }
 
-#' Get information of an lgb.Dataset object
+#' Get information of an \code{lgb.Dataset} object
 #'
 #' @param dataset Object of class \code{lgb.Dataset}
 #' @param name the name of the information field to get (see details)
@@ -901,8 +901,8 @@ slice.lgb.Dataset <- function(dataset, idxset, ...) {
 #' \itemize{
 #'     \item \code{label}: label lightgbm learn from ;
 #'     \item \code{weight}: to do a weight rescale ;
-#'     \item \code{group}: group size
-#'     \item \code{init_score}: initial score is the base prediction lightgbm will boost from ;
+#'     \item \code{group}: group size ;
+#'     \item \code{init_score}: initial score is the base prediction lightgbm will boost from.
 #' }
 #'
 #' @examples
@@ -937,9 +937,9 @@ getinfo.lgb.Dataset <- function(dataset, name, ...) {
 
 }
 
-#' Set information of an lgb.Dataset object
+#' Set information of an \code{lgb.Dataset} object
 #'
-#' @param dataset Object of class "lgb.Dataset"
+#' @param dataset Object of class \code{lgb.Dataset}
 #' @param name the name of the field to get
 #' @param info the specific field of information to set
 #' @param ... other parameters

R-package/R/lgb.prepare.R

 #' Data preparator for LightGBM datasets (numeric)
 #'
-#' Attempts to prepare a clean dataset to prepare to put in a lgb.Dataset. Factors and characters are converted to numeric without integers. Please use \code{lgb.prepare_rules} if you want to apply this transformation to other datasets.
+#' Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}. Factors and characters are converted to numeric without integers. Please use \code{lgb.prepare_rules} if you want to apply this transformation to other datasets.
 #'
 #' @param data A data.frame or data.table to prepare.
 #'
-#' @return The cleaned dataset. It must be converted to a matrix format (\code{as.matrix}) for input in lgb.Dataset.
+#' @return The cleaned dataset. It must be converted to a matrix format (\code{as.matrix}) for input in \code{lgb.Dataset}.
 #'
 #' @examples
 #' library(lightgbm)
 #' data(iris)
 #'
 #' str(iris)
-#' # 'data.frame':	150 obs. of  5 variables:
-#' # $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
-#' # $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
-#' # $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
-#' # $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
-#' # $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 ...
 #'
 #' str(lgb.prepare(data = iris)) # Convert all factors/chars to numeric
-#' # 'data.frame':	150 obs. of  5 variables:
-#' # $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
-#' # $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
-#' # $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
-#' # $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
-#' # $ Species     : num  1 1 1 1 1 1 1 1 1 1 ...
 #'
 #' \dontrun{
 #' # When lightgbm package is installed, and you do not want to load it

R-package/R/lgb.prepare2.R

 #' Data preparator for LightGBM datasets (integer)
 #'
-#' Attempts to prepare a clean dataset to prepare to put in a lgb.Dataset. Factors and characters are converted to numeric (specifically: integer). Please use \code{lgb.prepare_rules2} if you want to apply this transformation to other datasets. This is useful if you have a specific need for integer dataset instead of numeric dataset. Note that there are programs which do not support integer-only input. Consider this as a half memory technique which is dangerous, especially for LightGBM.
+#' Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}. Factors and characters are converted to numeric (specifically: integer). Please use \code{lgb.prepare_rules2} if you want to apply this transformation to other datasets. This is useful if you have a specific need for integer dataset instead of numeric dataset. Note that there are programs which do not support integer-only input. Consider this as a half memory technique which is dangerous, especially for LightGBM.
 #'
 #' @param data A data.frame or data.table to prepare.
 #'
-#' @return The cleaned dataset. It must be converted to a matrix format (\code{as.matrix}) for input in lgb.Dataset.
+#' @return The cleaned dataset. It must be converted to a matrix format (\code{as.matrix}) for input in \code{lgb.Dataset}.
 #'
 #' @examples
 #' library(lightgbm)
 #' data(iris)
 #'
 #' str(iris)
-#' # 'data.frame':	150 obs. of  5 variables:
-#' # $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
-#' # $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
-#' # $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
-#' # $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
-#' # $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 ...
 #'
 #' # Convert all factors/chars to integer
 #' str(lgb.prepare2(data = iris))
-#' # 'data.frame':	150 obs. of  5 variables:
-#' # $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
-#' # $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
-#' # $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
-#' # $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
-#' # $ Species     : int  1 1 1 1 1 1 1 1 1 1 ...
 #'
 #' \dontrun{
 #' # When lightgbm package is installed, and you do not want to load it

R-package/R/lgb.prepare_rules.R

 #' Data preparator for LightGBM datasets with rules (numeric)
 #'
-#' Attempts to prepare a clean dataset to prepare to put in a lgb.Dataset. Factors and characters are converted to numeric. In addition, keeps rules created so you can convert other datasets using this converter.
+#' Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}. Factors and characters are converted to numeric. In addition, keeps rules created so you can convert other datasets using this converter.
 #'
 #' @param data A data.frame or data.table to prepare.
 #' @param rules A set of rules from the data preparator, if already used.
 #'
-#' @return A list with the cleaned dataset (\code{data}) and the rules (\code{rules}). The data must be converted to a matrix format (\code{as.matrix}) for input in lgb.Dataset.
+#' @return A list with the cleaned dataset (\code{data}) and the rules (\code{rules}). The data must be converted to a matrix format (\code{as.matrix}) for input in \code{lgb.Dataset}.
 #'
 #' @examples
 #' library(lightgbm)
 #' data(iris)
 #'
 #' str(iris)
-#' # 'data.frame':	150 obs. of  5 variables:
-#' # $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
-#' # $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
-#' # $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
-#' # $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
-#' # $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 ...
 #'
 #' new_iris <- lgb.prepare_rules(data = iris) # Autoconverter
 #' str(new_iris$data)
-#' # 'data.frame':	150 obs. of  5 variables:
-#' # $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
-#' # $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
-#' # $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
-#' # $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
-#' # $ Species     : num  1 1 1 1 1 1 1 1 1 1 ...
 #'
 #' data(iris) # Erase iris dataset
 #' iris$Species[1] <- "NEW FACTOR" # Introduce junk factor (NA)
-#' # Warning message:
-#' # In `[<-.factor`(`*tmp*`, 1, value = c(NA, 1L, 1L, 1L, 1L, 1L, 1L,  :
-#' #  invalid factor level, NA generated
 #'
 #' # Use conversion using known rules
 #' # Unknown factors become 0, excellent for sparse datasets
@@ -40,14 +25,11 @@
 #'
 #' # Unknown factor is now zero, perfect for sparse datasets
 #' newer_iris$data[1, ] # Species became 0 as it is an unknown factor
-#' #   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
-#' # 1          5.1         3.5          1.4         0.2       0
 #'
 #' newer_iris$data[1, 5] <- 1 # Put back real initial value
 #'
 #' # Is the newly created dataset equal? YES!
 #' all.equal(new_iris$data, newer_iris$data)
-#' # [1] TRUE
 #'
 #' # Can we test our own rules?
 #' data(iris) # Erase iris dataset
@@ -58,12 +40,6 @@
 #'                                    "virginica" = 1))
 #' newest_iris <- lgb.prepare_rules(data = iris, rules = personal_rules)
 #' str(newest_iris$data) # SUCCESS!
-#' # 'data.frame':	150 obs. of  5 variables:
-#' # $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
-#' # $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
-#' # $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
-#' # $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
-#' # $ Species     : num  3 3 3 3 3 3 3 3 3 3 ...
 #'
 #' @importFrom data.table set
 #' @export

R-package/R/lgb.prepare_rules2.R

 #' Data preparator for LightGBM datasets with rules (integer)
 #'
-#' Attempts to prepare a clean dataset to prepare to put in a lgb.Dataset. Factors and characters are converted to numeric (specifically: integer). In addition, keeps rules created so you can convert other datasets using this converter. This is useful if you have a specific need for integer dataset instead of numeric dataset. Note that there are programs which do not support integer-only input. Consider this as a half memory technique which is dangerous, especially for LightGBM.
+#' Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}. Factors and characters are converted to numeric (specifically: integer). In addition, keeps rules created so you can convert other datasets using this converter. This is useful if you have a specific need for integer dataset instead of numeric dataset. Note that there are programs which do not support integer-only input. Consider this as a half memory technique which is dangerous, especially for LightGBM.
 #'
 #' @param data A data.frame or data.table to prepare.
 #' @param rules A set of rules from the data preparator, if already used.
 #'
-#' @return A list with the cleaned dataset (\code{data}) and the rules (\code{rules}). The data must be converted to a matrix format (\code{as.matrix}) for input in lgb.Dataset.
+#' @return A list with the cleaned dataset (\code{data}) and the rules (\code{rules}). The data must be converted to a matrix format (\code{as.matrix}) for input in \code{lgb.Dataset}.
 #'
 #' @examples
 #' library(lightgbm)
 #' data(iris)
 #'
 #' str(iris)
-#' # 'data.frame':	150 obs. of  5 variables:
-#' # $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
-#' # $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
-#' # $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
-#' # $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
-#' # $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 ...
 #'
 #' new_iris <- lgb.prepare_rules2(data = iris) # Autoconverter
 #' str(new_iris$data)
-#' # 'data.frame':	150 obs. of  5 variables:
-#' # $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
-#' # $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
-#' # $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
-#' # $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
-#' # $ Species     : int  1 1 1 1 1 1 1 1 1 1 ...
 #'
 #' data(iris) # Erase iris dataset
 #' iris$Species[1] <- "NEW FACTOR" # Introduce junk factor (NA)
-#' # Warning message:
-#' # In `[<-.factor`(`*tmp*`, 1, value = c(NA, 1L, 1L, 1L, 1L, 1L, 1L,  :
-#' #  invalid factor level, NA generated
 #'
 #' # Use conversion using known rules
 #' # Unknown factors become 0, excellent for sparse datasets
@@ -40,14 +25,11 @@
 #'
 #' # Unknown factor is now zero, perfect for sparse datasets
 #' newer_iris$data[1, ] # Species became 0 as it is an unknown factor
-#' #   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
-#' # 1          5.1         3.5          1.4         0.2       0
 #'
 #' newer_iris$data[1, 5] <- 1 # Put back real initial value
 #'
 #' # Is the newly created dataset equal? YES!
 #' all.equal(new_iris$data, newer_iris$data)
-#' # [1] TRUE
 #'
 #' # Can we test our own rules?
 #' data(iris) # Erase iris dataset
@@ -58,12 +40,6 @@
 #'                                    "virginica" = 1L))
 #' newest_iris <- lgb.prepare_rules2(data = iris, rules = personal_rules)
 #' str(newest_iris$data) # SUCCESS!
-#' # 'data.frame':	150 obs. of  5 variables:
-#' # $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
-#' # $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
-#' # $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
-#' # $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
-#' # $ Species     : int  3 3 3 3 3 3 3 3 3 3 ...
 #'
 #' @importFrom data.table set
 #' @export

R-package/R/readRDS.lgb.Booster.R

-#' readRDS for lgb.Booster models
+#' readRDS for \code{lgb.Booster} models
 #'
 #' Attempts to load a model using RDS.
 #'
 #' @param file a connection or the name of the file where the R object is saved to or read from.
 #' @param refhook a hook function for handling reference objects.
 #'
-#' @return lgb.Booster.
+#' @return \code{lgb.Booster}.
 #'
 #' @examples
 #' library(lightgbm)

R-package/R/saveRDS.lgb.Booster.R

-#' saveRDS for lgb.Booster models
+#' saveRDS for \code{lgb.Booster} models
 #'
 #' Attempts to save a model using RDS. Has an additional parameter (\code{raw}) which decides whether to save the raw model or not.
 #'

R-package/man/dim.Rd

@@ -2,7 +2,7 @@
 % Please edit documentation in R/lgb.Dataset.R
 \name{dim.lgb.Dataset}
 \alias{dim.lgb.Dataset}
-\title{Dimensions of an lgb.Dataset}
+\title{Dimensions of an \code{lgb.Dataset}}
 \usage{
 \method{dim}{lgb.Dataset}(x, ...)
 }

R-package/man/getinfo.Rd

@@ -3,7 +3,7 @@
 \name{getinfo}
 \alias{getinfo}
 \alias{getinfo.lgb.Dataset}
-\title{Get information of an lgb.Dataset object}
+\title{Get information of an \code{lgb.Dataset} object}
 \usage{
 getinfo(dataset, ...)
 
@@ -20,7 +20,7 @@ getinfo(dataset, ...)
 info data
 }
 \description{
-Get information of an lgb.Dataset object
+Get information of an \code{lgb.Dataset} object
 }
 \details{
 The \code{name} field can be one of the following:
@@ -28,8 +28,8 @@ The \code{name} field can be one of the following:
 \itemize{
     \item \code{label}: label lightgbm learn from ;
     \item \code{weight}: to do a weight rescale ;
-    \item \code{group}: group size
-    \item \code{init_score}: initial score is the base prediction lightgbm will boost from ;
+    \item \code{group}: group size ;
+    \item \code{init_score}: initial score is the base prediction lightgbm will boost from.
 }
 }
 \examples{

R-package/man/lgb.Dataset.Rd

@@ -2,7 +2,7 @@
 % Please edit documentation in R/lgb.Dataset.R
 \name{lgb.Dataset}
 \alias{lgb.Dataset}
-\title{Construct lgb.Dataset object}
+\title{Construct \code{lgb.Dataset} object}
 \usage{
 lgb.Dataset(data, params = list(), reference = NULL, colnames = NULL,
   categorical_feature = NULL, free_raw_data = TRUE, info = list(),
@@ -21,7 +21,7 @@ lgb.Dataset(data, params = list(), reference = NULL, colnames = NULL,
 
 \item{free_raw_data}{TRUE for need to free raw data after construct}
 
-\item{info}{a list of information of the lgb.Dataset object}
+\item{info}{a list of information of the \code{lgb.Dataset} object}
 
 \item{...}{other information to pass to \code{info} or parameters pass to \code{params}}
 }
@@ -29,7 +29,7 @@ lgb.Dataset(data, params = list(), reference = NULL, colnames = NULL,
 constructed dataset
 }
 \description{
-Construct lgb.Dataset object from dense matrix, sparse matrix
+Construct \code{lgb.Dataset} object from dense matrix, sparse matrix
 or local file (that was created previously by saving an \code{lgb.Dataset}).
 }
 \examples{

R-package/man/lgb.Dataset.create.valid.Rd

@@ -11,7 +11,7 @@ lgb.Dataset.create.valid(dataset, data, info = list(), ...)
 
 \item{data}{a \code{matrix} object, a \code{dgCMatrix} object or a character representing a filename}
 
-\item{info}{a list of information of the lgb.Dataset object}
+\item{info}{a list of information of the \code{lgb.Dataset} object}
 
 \item{...}{other information to pass to \code{info}.}
 }

R-package/man/lgb.Dataset.save.Rd

@@ -18,7 +18,6 @@ passed dataset
 Save \code{lgb.Dataset} to a binary file
 }
 \examples{
-
 library(lightgbm)
 data(agaricus.train, package = "lightgbm")
 train <- agaricus.train

R-package/man/lgb.cv.Rd

@@ -92,8 +92,8 @@ params <- list(objective = "regression", metric = "l2")
 model <- lgb.cv(params,
                 dtrain,
                 10,
-                nfold = 5,
+                nfold = 3,
                 min_data = 1,
                 learning_rate = 1,
-                early_stopping_rounds = 10)
+                early_stopping_rounds = 5)
 }

R-package/man/lgb.dump.Rd

@@ -29,11 +29,11 @@ params <- list(objective = "regression", metric = "l2")
 valids <- list(test = dtest)
 model <- lgb.train(params,
                   dtrain,
-                   100,
+                   10,
                    valids,
                    min_data = 1,
                    learning_rate = 1,
-                   early_stopping_rounds = 10)
+                   early_stopping_rounds = 5)
 json_model <- lgb.dump(model)
 
 }

R-package/man/lgb.get.eval.result.Rd

@@ -36,11 +36,10 @@ params <- list(objective = "regression", metric = "l2")
 valids <- list(test = dtest)
 model <- lgb.train(params,
                    dtrain,
-                   100,
+                   10,
                    valids,
                    min_data = 1,
                    learning_rate = 1,
-                   early_stopping_rounds = 10)
+                   early_stopping_rounds = 5)
 lgb.get.eval.result(model, "test", "l2")
-
 }

R-package/man/lgb.importance.Rd

@@ -30,10 +30,9 @@ train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
 
 params <- list(objective = "binary",
-              learning_rate = 0.01, num_leaves = 63, max_depth = -1,
-              min_data_in_leaf = 1, min_sum_hessian_in_leaf = 1)
-              model <- lgb.train(params, dtrain, 20)
-model <- lgb.train(params, dtrain, 20)
+               learning_rate = 0.01, num_leaves = 63, max_depth = -1,
+               min_data_in_leaf = 1, min_sum_hessian_in_leaf = 1)
+model <- lgb.train(params, dtrain, 10)
 
 tree_imp1 <- lgb.importance(model, percentage = TRUE)
 tree_imp2 <- lgb.importance(model, percentage = FALSE)

R-package/man/lgb.interprete.Rd

@@ -44,7 +44,7 @@ params <- list(
     , min_data_in_leaf = 1
     , min_sum_hessian_in_leaf = 1
 )
-model <- lgb.train(params, dtrain, 20)
+model <- lgb.train(params, dtrain, 10)
 
 tree_interpretation <- lgb.interprete(model, test$data, 1:5)
 

R-package/man/lgb.load.Rd

@@ -31,11 +31,11 @@ params <- list(objective = "regression", metric = "l2")
 valids <- list(test = dtest)
 model <- lgb.train(params,
                    dtrain,
-                   100,
+                   10,
                    valids,
                    min_data = 1,
                    learning_rate = 1,
-                   early_stopping_rounds = 10)
+                   early_stopping_rounds = 5)
 lgb.save(model, "model.txt")
 load_booster <- lgb.load(filename = "model.txt")
 model_string <- model$save_model_to_string(NULL) # saves best iteration

R-package/man/lgb.model.dt.tree.Rd

@@ -45,10 +45,9 @@ train <- agaricus.train
 dtrain <- lgb.Dataset(train$data, label = train$label)
 
 params <- list(objective = "binary",
-              learning_rate = 0.01, num_leaves = 63, max_depth = -1,
-              min_data_in_leaf = 1, min_sum_hessian_in_leaf = 1)
-              model <- lgb.train(params, dtrain, 20)
-model <- lgb.train(params, dtrain, 20)
+               learning_rate = 0.01, num_leaves = 63, max_depth = -1,
+               min_data_in_leaf = 1, min_sum_hessian_in_leaf = 1)
+model <- lgb.train(params, dtrain, 10)
 
 tree_dt <- lgb.model.dt.tree(model)
 

R-package/man/lgb.plot.importance.Rd

@@ -43,7 +43,7 @@ params <- list(
     , min_sum_hessian_in_leaf = 1
 )
 
-model <- lgb.train(params, dtrain, 20)
+model <- lgb.train(params, dtrain, 10)
 
 tree_imp <- lgb.importance(model, percentage = TRUE)
 lgb.plot.importance(tree_imp, top_n = 10, measure = "Gain")