Predict the outcome of a new observation based on multiple trees.
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## S3 method for class 'classbagg' predict(object, newdata=NULL, type=c("class", "prob"), aggregation=c("majority", "average", "weighted"), ...) ## S3 method for class 'regbagg' predict(object, newdata=NULL, aggregation=c("average", "weighted"), ...) ## S3 method for class 'survbagg' predict(object, newdata=NULL,...)
object of classes
a data frame of new observations.
character string denoting the type of predicted value
returned for classification trees. Either
character string specifying how to aggregate, see below.
additional arguments, currently not passed to any function.
There are (at least) three different ways to aggregate the predictions of
bagging classification trees. Most famous is class majority voting
aggregation="majority") where the most frequent class is returned. The
second way is choosing the class with maximal averaged class probability
aggregation="average"). The third method is based on the "aggregated learning
sample", introduced by Hothorn et al. (2003) for survival trees.
The prediction of a new observation is the majority class, mean or
Kaplan-Meier curve of all observations from the learning sample
identified by the
nbagg leaves containing the new observation.
For regression trees, only averaged or weighted predictions are possible.
By default, the out-of-bag estimate is computed if
newdata is NOT
specified. Therefore, the predictions of
predict(object) are "honest"
in some way (this is not possible for combined models via
If you like to compute the predictions for the learning sample
newdata to specify your data.
The predicted class or estimated class probabilities are returned for classification trees. The predicted endpoint is returned in regression problems and the predicted Kaplan-Meier curve is returned for survival trees.
Leo Breiman (1996), Bagging Predictors. Machine Learning 24(2), 123–140.
Torsten Hothorn, Berthold Lausen, Axel Benner and Martin Radespiel-Troeger (2004), Bagging Survival Trees. Statistics in Medicine, 23(1), 77–91.
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data("Ionosphere", package = "mlbench") Ionosphere$V2 <- NULL # constant within groups # nbagg = 10 for performance reasons here mod <- bagging(Class ~ ., data=Ionosphere) # out-of-bag estimate mean(predict(mod) != Ionosphere$Class) # predictions for the first 10 observations predict(mod, newdata=Ionosphere[1:10,]) predict(mod, newdata=Ionosphere[1:10,], type="prob")
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