ipred (version 0.9-15)

predict.classbagg: Predictions from Bagging Trees

Description

Predict the outcome of a new observation based on multiple trees.

Usage

# S3 method for classbagg
predict(object, newdata=NULL, type=c("class", "prob"),
                            aggregation=c("majority", "average", "weighted"), ...)
# S3 method for regbagg
predict(object, newdata=NULL, aggregation=c("average",
                "weighted"), ...)
# S3 method for survbagg
predict(object, newdata=NULL,...)

Value

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.

Arguments

object

object of classes classbagg, regbagg or survbagg.

newdata

a data frame of new observations.

type

character string denoting the type of predicted value returned for classification trees. Either class (predicted classes are returned) or prob (estimated class probabilities are returned).

aggregation

character string specifying how to aggregate, see below.

...

additional arguments, currently not passed to any function.

Details

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 comb in bagging). If you like to compute the predictions for the learning sample itself, use newdata to specify your data.

References

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.

Examples

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")