Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

refine style with max.depth

Browse Source
This commit is contained in:
hetong 2014-09-06 23:20:11 -07:00
parent 1925321a16
commit 9e3b878943
7 changed files with 11 additions and 11 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
R-package/R/xgb.cv.R
View File

@ -10,7 +10,7 @@
#' \item \code{binary:logistic} logistic regression for classification #' \item \code{binary:logistic} logistic regression for classification
#' } #' }
#' \item \code{eta} step size of each boosting step #' \item \code{eta} step size of each boosting step
#' \item \code{max_depth} maximum depth of the tree #' \item \code{max.depth} maximum depth of the tree
#' \item \code{nthread} number of thread used in training, if not set, all threads are used #' \item \code{nthread} number of thread used in training, if not set, all threads are used
#' } #' }
#' #'
@ -50,7 +50,7 @@
#' data(agaricus.train, package='xgboost') #' data(agaricus.train, package='xgboost')
#' dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label) #' dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
#' history <- xgb.cv(data = dtrain, nround=3, nfold = 5, metrics=list("rmse","auc"), #' history <- xgb.cv(data = dtrain, nround=3, nfold = 5, metrics=list("rmse","auc"),
#' "max_depth"=3, "eta"=1, "objective"="binary:logistic") #' "max.depth"=3, "eta"=1, "objective"="binary:logistic")
#' @export #' @export
#' #'
xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL,

4
R-package/R/xgb.train.R
View File

@ -10,7 +10,7 @@
#' \item \code{binary:logistic} logistic regression for classification #' \item \code{binary:logistic} logistic regression for classification
#' } #' }
#' \item \code{eta} step size of each boosting step #' \item \code{eta} step size of each boosting step
#' \item \code{max_depth} maximum depth of the tree #' \item \code{max.depth} maximum depth of the tree
#' \item \code{nthread} number of thread used in training, if not set, all threads are used #' \item \code{nthread} number of thread used in training, if not set, all threads are used
#' } #' }
#' #'
@ -50,7 +50,7 @@
#' dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label) #' dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
#' dtest <- dtrain #' dtest <- dtrain
#' watchlist <- list(eval = dtest, train = dtrain) #' watchlist <- list(eval = dtest, train = dtrain)
#' param <- list(max_depth = 2, eta = 1, silent = 1) #' param <- list(max.depth = 2, eta = 1, silent = 1)
#' logregobj <- function(preds, dtrain) { #' logregobj <- function(preds, dtrain) {
#' labels <- getinfo(dtrain, "label") #' labels <- getinfo(dtrain, "label")
#' preds <- 1/(1 + exp(-preds)) #' preds <- 1/(1 + exp(-preds))

2
R-package/R/xgboost.R
View File

@ -14,7 +14,7 @@
#' \item \code{binary:logistic} logistic regression for classification #' \item \code{binary:logistic} logistic regression for classification
#' } #' }
#' \item \code{eta} step size of each boosting step #' \item \code{eta} step size of each boosting step
#' \item \code{max_depth} maximum depth of the tree #' \item \code{max.depth} maximum depth of the tree
#' \item \code{nthread} number of thread used in training, if not set, all threads are used #' \item \code{nthread} number of thread used in training, if not set, all threads are used
#' } #' }
#' #'

4
R-package/man/xgb.cv.Rd
View File

@ -15,7 +15,7 @@ xgb.cv(params = list(), data, nrounds, nfold, label = NULL, showsd = TRUE,
\item \code{binary:logistic} logistic regression for classification \item \code{binary:logistic} logistic regression for classification
} }
\item \code{eta} step size of each boosting step \item \code{eta} step size of each boosting step
\item \code{max_depth} maximum depth of the tree \item \code{max.depth} maximum depth of the tree
\item \code{nthread} number of thread used in training, if not set, all threads are used \item \code{nthread} number of thread used in training, if not set, all threads are used
} }
@ -67,6 +67,6 @@ This function only accepts an \code{xgb.DMatrix} object as the input.
data(agaricus.train, package='xgboost') data(agaricus.train, package='xgboost')
dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label) dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
history <- xgb.cv(data = dtrain, nround=3, nfold = 5, metrics=list("rmse","auc"), history <- xgb.cv(data = dtrain, nround=3, nfold = 5, metrics=list("rmse","auc"),
"max_depth"=3, "eta"=1, "objective"="binary:logistic") "max.depth"=3, "eta"=1, "objective"="binary:logistic")
} }

4
R-package/man/xgb.train.Rd
View File

@ -15,7 +15,7 @@ xgb.train(params = list(), data, nrounds, watchlist = list(), obj = NULL,
\item \code{binary:logistic} logistic regression for classification \item \code{binary:logistic} logistic regression for classification
} }
\item \code{eta} step size of each boosting step \item \code{eta} step size of each boosting step
\item \code{max_depth} maximum depth of the tree \item \code{max.depth} maximum depth of the tree
\item \code{nthread} number of thread used in training, if not set, all threads are used \item \code{nthread} number of thread used in training, if not set, all threads are used
} }
@ -62,7 +62,7 @@ data(agaricus.train, package='xgboost')
dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label) dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
dtest <- dtrain dtest <- dtrain
watchlist <- list(eval = dtest, train = dtrain) watchlist <- list(eval = dtest, train = dtrain)
param <- list(max_depth = 2, eta = 1, silent = 1) param <- list(max.depth = 2, eta = 1, silent = 1)
logregobj <- function(preds, dtrain) { logregobj <- function(preds, dtrain) {
labels <- getinfo(dtrain, "label") labels <- getinfo(dtrain, "label")
preds <- 1/(1 + exp(-preds)) preds <- 1/(1 + exp(-preds))

2
R-package/man/xgboost.Rd
View File

@ -20,7 +20,7 @@ xgboost(data = NULL, label = NULL, params = list(), nrounds,
\item \code{binary:logistic} logistic regression for classification \item \code{binary:logistic} logistic regression for classification
} }
\item \code{eta} step size of each boosting step \item \code{eta} step size of each boosting step
\item \code{max_depth} maximum depth of the tree \item \code{max.depth} maximum depth of the tree
\item \code{nthread} number of thread used in training, if not set, all threads are used \item \code{nthread} number of thread used in training, if not set, all threads are used
} }

2
R-package/vignettes/xgboost.Rnw
View File

@ -162,7 +162,7 @@ evalerror <- function(preds, dtrain) {
dtest <- xgb.DMatrix(test$data, label = test$label) dtest <- xgb.DMatrix(test$data, label = test$label)
watchlist <- list(eval = dtest, train = dtrain) watchlist <- list(eval = dtest, train = dtrain)
param <- list(max_depth = 2, eta = 1, silent = 1) param <- list(max.depth = 2, eta = 1, silent = 1)
bst <- xgb.train(param, dtrain, nround = 2, watchlist, logregobj, evalerror) bst <- xgb.train(param, dtrain, nround = 2, watchlist, logregobj, evalerror)
@ @