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Added test for eta decay (+3 squashed commits)

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Squashed commits:
[9109887] Added test for eta decay(+1 squashed commit)
Squashed commits:
[1336bd4] Added tests for eta decay (+2 squashed commit)
Squashed commits:
[91aac2d] Added tests for eta decay (+1 squashed commit)
Squashed commits:
[3ff48e7] Added test for eta decay
[6bb1eed] Rewrote Rd files
[bf0dec4] Added learning_rates for diff eta in each boosting round
This commit is contained in:
terrytangyuan
2015-10-31 10:35:01 -04:00
parent c817efbd8a
commit 888edba03f
6 changed files with 93 additions and 62 deletions
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2
R-package/man/predict-xgb.Booster-method.Rd
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@@ -5,7 +5,7 @@
\alias{predict,xgb.Booster-method}
\title{Predict method for eXtreme Gradient Boosting model}
\usage{
\S4method{predict}{xgb.Booster}(object, newdata, missing = NULL,
\S4method{predict}{xgb.Booster}(object, newdata, missing = NA,
outputmargin = FALSE, ntreelimit = NULL, predleaf = FALSE)
}
\arguments{

2
R-package/man/xgb.DMatrix.Rd
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@@ -4,7 +4,7 @@
\alias{xgb.DMatrix}
\title{Contruct xgb.DMatrix object}
\usage{
xgb.DMatrix(data, info = list(), missing = 0, ...)
xgb.DMatrix(data, info = list(), missing = NA, ...)
}
\arguments{
\item{data}{a \code{matrix} object, a \code{dgCMatrix} object or a character

9
R-package/man/xgb.cv.Rd
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@@ -4,11 +4,10 @@
\alias{xgb.cv}
\title{Cross Validation}
\usage{
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, print.every.n = 1L, early.stop.round = NULL,
maximize = NULL, ...)
xgb.cv(params = list(), data, nrounds, nfold, label = NULL, missing = NA,
prediction = FALSE, showsd = TRUE, metrics = list(), obj = NULL,
feval = NULL, stratified = TRUE, folds = NULL, verbose = T,
print.every.n = 1L, early.stop.round = NULL, maximize = NULL, ...)
}
\arguments{
\item{params}{the list of parameters. Commonly used ones are:

2
R-package/man/xgboost.Rd
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@@ -4,7 +4,7 @@
\alias{xgboost}
\title{eXtreme Gradient Boosting (Tree) library}
\usage{
xgboost(data = NULL, label = NULL, missing = NULL, weight = NULL,
xgboost(data = NULL, label = NULL, missing = NA, weight = NULL,
params = list(), nrounds, verbose = 1, print.every.n = 1L,
early.stop.round = NULL, maximize = NULL, save_period = 0,
save_name = "xgboost.model", ...)