888edba03f
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
44 lines
1.6 KiB
R
44 lines
1.6 KiB
R
% Generated by roxygen2 (4.1.1): do not edit by hand
|
|
% Please edit documentation in R/predict.xgb.Booster.R
|
|
\docType{methods}
|
|
\name{predict,xgb.Booster-method}
|
|
\alias{predict,xgb.Booster-method}
|
|
\title{Predict method for eXtreme Gradient Boosting model}
|
|
\usage{
|
|
\S4method{predict}{xgb.Booster}(object, newdata, missing = NA,
|
|
outputmargin = FALSE, ntreelimit = NULL, predleaf = FALSE)
|
|
}
|
|
\arguments{
|
|
\item{object}{Object of class "xgb.Boost"}
|
|
|
|
\item{newdata}{takes \code{matrix}, \code{dgCMatrix}, local data file or
|
|
\code{xgb.DMatrix}.}
|
|
|
|
\item{missing}{Missing is only used when input is dense matrix, pick a float
|
|
value that represents missing value. Sometime a data use 0 or other extreme value to represents missing values.}
|
|
|
|
\item{outputmargin}{whether the prediction should be shown in the original
|
|
value of sum of functions, when outputmargin=TRUE, the prediction is
|
|
untransformed margin value. In logistic regression, outputmargin=T will
|
|
output value before logistic transformation.}
|
|
|
|
\item{ntreelimit}{limit number of trees used in prediction, this parameter is
|
|
only valid for gbtree, but not for gblinear. set it to be value bigger
|
|
than 0. It will use all trees by default.}
|
|
|
|
\item{predleaf}{whether predict leaf index instead. If set to TRUE, the output will be a matrix object.}
|
|
}
|
|
\description{
|
|
Predicted values based on xgboost model object.
|
|
}
|
|
\examples{
|
|
data(agaricus.train, package='xgboost')
|
|
data(agaricus.test, package='xgboost')
|
|
train <- agaricus.train
|
|
test <- agaricus.test
|
|
bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
|
|
eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
|
|
pred <- predict(bst, test$data)
|
|
}
|
|
|