predict.bagging {adabag}R Documentation

Predicts from a fitted bagging object.

Description

Classifies a dataframe using a fitted bagging object.

Usage

## S3 method for class 'bagging':
predict(object, newdata, ...)

Arguments

object fitted model object of class bagging. This is assumed to be the result of some function that produces an object with the same named components as that returned by the bagging function.
newdata data frame containing the values at which predictions are required. The predictors referred to in the right side of formula(object) must be present by name in newdata.
... further arguments passed to or from other methods.

Value

An object of class predict.bagging, which is a list with the following components:

class the class predicted by the ensemble classifier.
confusion the confusion matrix which compares the real class with the predicted one.
error returns the average error.

Author(s)

Esteban Alfaro Cortes Esteban.Alfaro@uclm.es, Matias Gamez Martinez Matias.Gamez@uclm.es and Noelia Garcia Rubio Noelia.Garcia@uclm.es

References

Alfaro, E., Gamez, M. and Garcia, N. (2007): ``Multiclass corporate failure prediction by Adaboost.M1''. International Advances in Economic Research, Vol 13, 3, pp. 301–312.

Breiman, L. (1996): "Bagging predictors". Machine Learning, Vol 24, 2, pp. 123–140.

Breiman, L. (1998). "Arcing classifiers". The Annals of Statistics, Vol 26, 3, pp. 801–849.

See Also

bagging, bagging.cv

Examples

library(rpart)
data(iris)
names(iris)<-c("LS","AS","LP","AP","Especies")
sub <- c(sample(1:50, 25), sample(51:100, 25), sample(101:150, 25))
iris.bagging <- bagging(Especies ~ ., data=iris[sub,], mfinal=10)
iris.predbagging<- predict.bagging(iris.bagging, newdata=iris[-sub,])

## rpart and mlbench libraries should be loaded
library(rpart)
library(mlbench)
data(BreastCancer)
l <- length(BreastCancer[,1])
sub <- sample(1:l,2*l/3)
BC.bagging <- bagging(Class ~.,data=BreastCancer[,-1],mfinal=25, maxdepth=3)
BC.bagging.pred <- predict.bagging(BC.bagging,newdata=BreastCancer[-sub,-1])
BC.bagging.pred[-1]

# Data Vehicle (four classes)
library(rpart)
library(mlbench)
data(Vehicle)
l <- length(Vehicle[,1])
sub <- sample(1:l,2*l/3)
Vehicle.bagging <- bagging(Class ~.,data=Vehicle[sub, ],mfinal=50, maxdepth=5)
Vehicle.bagging.pred <- predict.bagging(Vehicle.bagging,newdata=Vehicle[-sub, ])
Vehicle.bagging.pred[-1]


[Package adabag version 1.1 Index]