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Deprecate reg:linear' in favor of reg:squarederror'. (#4267)

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* Deprecate `reg:linear' in favor of `reg:squarederror'.
* Replace the use of `reg:linear'.
* Replace the use of `silent`.
This commit is contained in:
Jiaming Yuan
2019-03-17 17:55:04 +08:00
committed by GitHub
parent cf8d5b9b76
commit 29a1356669
34 changed files with 210 additions and 193 deletions
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2
R-package/man/xgb.cv.Rd
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@@ -16,7 +16,7 @@ xgb.cv(params = list(), data, nrounds, nfold, label = NULL,
\itemize{
\item \code{objective} objective function, common ones are
\itemize{
\item \code{reg:linear} linear regression
\item \code{reg:squarederror} Regression with squared loss.
\item \code{binary:logistic} logistic regression for classification
}
\item \code{eta} step size of each boosting step

10
R-package/man/xgb.train.Rd
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@@ -56,7 +56,7 @@ xgboost(data = NULL, label = NULL, missing = NA, weight = NULL,
\itemize{
\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:squarederror} Regression with squared loss (Default).
\item \code{reg:logistic} logistic regression.
\item \code{binary:logistic} logistic regression for binary classification. Output probability.
\item \code{binary:logitraw} logistic regression for binary classification, output score before logistic transformation.
@@ -210,7 +210,7 @@ dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
watchlist <- list(train = dtrain, eval = dtest)
## A simple xgb.train example:
param <- list(max_depth = 2, eta = 1, silent = 1, nthread = 2,
param <- list(max_depth = 2, eta = 1, verbosity = 0, nthread = 2,
objective = "binary:logistic", eval_metric = "auc")
bst <- xgb.train(param, dtrain, nrounds = 2, watchlist)
@@ -231,12 +231,12 @@ evalerror <- function(preds, dtrain) {
# These functions could be used by passing them either:
# as 'objective' and 'eval_metric' parameters in the params list:
param <- list(max_depth = 2, eta = 1, silent = 1, nthread = 2,
param <- list(max_depth = 2, eta = 1, verbosity = 0, nthread = 2,
objective = logregobj, eval_metric = evalerror)
bst <- xgb.train(param, dtrain, nrounds = 2, watchlist)
# or through the ... arguments:
param <- list(max_depth = 2, eta = 1, silent = 1, nthread = 2)
param <- list(max_depth = 2, eta = 1, verbosity = 0, nthread = 2)
bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
objective = logregobj, eval_metric = evalerror)
@@ -246,7 +246,7 @@ bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,
## An xgb.train example of using variable learning rates at each iteration:
param <- list(max_depth = 2, eta = 1, silent = 1, nthread = 2,
param <- list(max_depth = 2, eta = 1, verbosity = 0, nthread = 2,
objective = "binary:logistic", eval_metric = "auc")
my_etas <- list(eta = c(0.5, 0.1))
bst <- xgb.train(param, dtrain, nrounds = 2, watchlist,