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change doc and demo for new obj feval interface

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hetong007
2015-05-25 11:30:04 -07:00
parent 19b24cf978
commit 8d3a7e1688
5 changed files with 17 additions and 15 deletions
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8
R-package/R/xgb.train.R
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@@ -36,7 +36,7 @@
#' 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.
@@ -48,7 +48,7 @@
#' \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.
#' }
#'
#' @param data takes an \code{xgb.DMatrix} as the input.
@@ -103,7 +103,6 @@
#' 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))
@@ -116,7 +115,8 @@
#' 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)
#' @export
#'
xgb.train <- function(params=list(), data, nrounds, watchlist = list(),