change doc and demo for new obj feval interface

R-package/man/xgb.cv.Rd

@@ -7,8 +7,8 @@
 xgb.cv(params = list(), data, nrounds, nfold, label = NULL,
   missing = NULL, prediction = FALSE, showsd = TRUE, metrics = list(),
   obj = NULL, feval = NULL, stratified = TRUE, folds = NULL,
-  verbose = T, printEveryN=1L, early_stop_round = NULL, early.stop.round = NULL,
-  maximize = NULL, ...)
+  verbose = T, printEveryN = 1L, early_stop_round = NULL,
+  early.stop.round = NULL, maximize = NULL, ...)
 }
 \arguments{
 \item{params}{the list of parameters. Commonly used ones are:

R-package/man/xgb.train.Rd

@@ -5,7 +5,7 @@
 \title{eXtreme Gradient Boosting Training}
 \usage{
 xgb.train(params = list(), data, nrounds, watchlist = list(), obj = NULL,
-  feval = NULL, verbose = 1, printEveryN=1L, early_stop_round = NULL,
+  feval = NULL, verbose = 1, printEveryN = 1L, early_stop_round = NULL,
   early.stop.round = NULL, maximize = NULL, ...)
 }
 \arguments{
@@ -43,7 +43,7 @@ xgb.train(params = list(), data, nrounds, watchlist = list(), obj = NULL,
 3. Task Parameters
 
 \itemize{
-\item \code{objective} specify the learning task and the corresponding learning objective, and the objective options are below:
+\item \code{objective} specify the learning task and the corresponding learning objective, users can pass a self-defined function to it. The default objective options are below:
   \itemize{
     \item \code{reg:linear} linear regression (Default).
     \item \code{reg:logistic} logistic regression.
@@ -55,7 +55,7 @@ xgb.train(params = list(), data, nrounds, watchlist = list(), obj = NULL,
     \item \code{rank:pairwise} set xgboost to do ranking task by minimizing the pairwise loss.
   }
   \item \code{base_score} the initial prediction score of all instances, global bias. Default: 0.5
-  \item \code{eval_metric} evaluation metrics for validation data. Default: metric will be assigned according to objective(rmse for regression, and error for classification, mean average precision for ranking). List is provided in detail section.
+  \item \code{eval_metric} evaluation metrics for validation data. Users can pass a self-defined function to it. Default: metric will be assigned according to objective(rmse for regression, and error for classification, mean average precision for ranking). List is provided in detail section.
 }}
 
 \item{data}{takes an \code{xgb.DMatrix} as the input.}
@@ -122,7 +122,6 @@ data(agaricus.train, package='xgboost')
 dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
 dtest <- dtrain
 watchlist <- list(eval = dtest, train = dtrain)
-param <- list(max.depth = 2, eta = 1, silent = 1)
 logregobj <- function(preds, dtrain) {
    labels <- getinfo(dtrain, "label")
    preds <- 1/(1 + exp(-preds))
@@ -135,6 +134,7 @@ evalerror <- function(preds, dtrain) {
   err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
   return(list(metric = "error", value = err))
 }
-bst <- xgb.train(param, dtrain, nthread = 2, nround = 2, watchlist, logregobj, evalerror)
+param <- list(max.depth = 2, eta = 1, silent = 1, objective=logregobj,eval_metric=evalerror)
+bst <- xgb.train(param, dtrain, nthread = 2, nround = 2, watchlist)
 }
 

R-package/man/xgboost.Rd

@@ -5,8 +5,8 @@
 \title{eXtreme Gradient Boosting (Tree) library}
 \usage{
 xgboost(data = NULL, label = NULL, missing = NULL, params = list(),
-  nrounds, verbose = 1, printEveryN=1L, early_stop_round = NULL, early.stop.round = NULL,
-  maximize = NULL, ...)
+  nrounds, verbose = 1, printEveryN = 1L, early_stop_round = NULL,
+  early.stop.round = NULL, maximize = NULL, ...)
 }
 \arguments{
 \item{data}{takes \code{matrix}, \code{dgCMatrix}, local data file or