predict,xgb.Booster-method

From xgboost v0.4-2 by Tong He

Predict method for eXtreme Gradient Boosting model

Predicted values based on xgboost model object.

Usage

## S3 method for class 'xgb.Booster':
predict(object, newdata, missing = NULL,
  outputmargin = FALSE, ntreelimit = NULL, predleaf = FALSE)

Arguments

object: Object of class "xgb.Boost"
newdata: takes matrix, dgCMatrix, local data file or xgb.DMatrix.
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.
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.
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.
predleaf: whether predict leaf index instead. If set to TRUE, the output will be a matrix 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)

Documentation reproduced from package xgboost, version 0.4-2, License: Apache License (== 2.0) | file LICENSE

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