Description Usage Arguments Details Value References Examples

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

1 2 3 4 5 6 7 8 | ```
## 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` |
object of classes |

`newdata` |
a data frame of new observations. |

`type` |
character string denoting the type of predicted value
returned for classification trees. Either |

`aggregation` |
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 `comb`

in
`bagging`

).
If you like to compute the predictions for the learning sample
itself, use `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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
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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