predict.bagging: Predicts from a fitted bagging object

In adabag: Applies Multiclass AdaBoost.M1, SAMME and Bagging

Predicts from a fitted bagging object

Description

Classifies a dataframe using a fitted bagging object.

Usage

## S3 method for class 'bagging'
predict(object, newdata, newmfinal=length(object$trees), ...)

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.
newmfinal	The number of trees of the bagging object to be used in the prediction. This argument allows the user to prune the ensemble. By default all the trees in the bagging object are used
...	further arguments passed to or from other methods.

Value

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

formula	the formula used.
votes	a matrix describing, for each observation, the number of trees that assigned it to each class.
prob	a matrix describing, for each observation, the posterior probability or degree of support of each class. These probabilities are calculated using the proportion of votes in the final ensemble.
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 [e-mail], Matias Gamez-Martinez [e-mail] and Noelia Garcia-Rubio [e-mail]

References

Alfaro, E., Gamez, M. and Garcia, N. (2013): “adabag: An R Package for Classification with Boosting and Bagging”. Journal of Statistical Software, Vol 54, 2, pp. 1–35.

Alfaro, E., Garcia, N., Gamez, M. and Elizondo, D. (2008): “Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks”. Decision Support Systems, 45, pp. 110–122.

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)
#sub <- c(sample(1:50, 25), sample(51:100, 25), sample(101:150, 25))
#iris.bagging <- bagging(Species ~ ., data=iris[sub,], mfinal=5)
#iris.predbagging<- predict.bagging(iris.bagging, newdata=iris[-sub,])
#iris.predbagging

## 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=5, 
control=rpart.control(maxdepth=3))
BC.bagging.pred <- predict.bagging(BC.bagging,newdata=BreastCancer[-sub,-1])
BC.bagging.pred$prob
BC.bagging.pred$confusion
BC.bagging.pred$error

adabag documentation built on May 31, 2023, 9:09 p.m.