predict.boosting {adabag}R Documentation

Predicts from a fitted Adaboost.M1 object.

Description

Classifies a dataframe using a fitted adaboost.M1 object.

Usage

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

Arguments

object fitted model object of class adaboost.M1. This is assumed to be the result of some function that produces an object with the same named components as that returned by the adaboost.M1 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.boosting, 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.

Freund, Y. and Schapire, R.E. (1996): "Experiments with a New Boosting Algorithm". En Proceedings of the Thirteenth International Conference on Machine Learning, pp. 148–156, Morgan Kaufmann.

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

See Also

adaboost.M1, boosting.cv

Examples

## rpart library should be loaded
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.adaboost <- adaboost.M1(Especies ~ ., data=iris[sub,], mfinal=10)
iris.predboosting<- predict.boosting(iris.adaboost, newdata=iris[-sub,])

## rpart and mlbench libraries should be loaded
## Comparing the test error of rpart and adaboost.M1
library(rpart)
library(mlbench)
data(BreastCancer)
l <- length(BreastCancer[,1])
sub <- sample(1:l,2*l/3)

BC.rpart <- rpart(Class~.,data=BreastCancer[sub,-1], maxdepth=3)
BC.rpart.pred <- predict(BC.rpart,newdata=BreastCancer[-sub,-1],type="class")
tb <-table(BC.rpart.pred,BreastCancer$Class[-sub])
error.rpart <- 1-(sum(diag(tb))/sum(tb))
tb
error.rpart

BC.adaboost <- adaboost.M1(Class ~.,data=BreastCancer[,-1],mfinal=25, maxdepth=3)
BC.adaboost.pred <- predict.boosting(BC.adaboost,newdata=BreastCancer[-sub,-1])
BC.adaboost.pred[-1]

## Data Vehicle (four classes) 
library(rpart)
library(mlbench)
data(Vehicle)
l <- length(Vehicle[,1])
sub <- sample(1:l,2*l/3)
mfinal <- 25
maxdepth <- 5

Vehicle.rpart <- rpart(Class~.,data=Vehicle[sub,],maxdepth=maxdepth)
Vehicle.rpart.pred <- predict(Vehicle.rpart,newdata=Vehicle[-sub, ],type="class")
tb <- table(Vehicle.rpart.pred,Vehicle$Class[-sub])
error.rpart <- 1-(sum(diag(tb))/sum(tb))
tb
error.rpart

Vehicle.adaboost <- adaboost.M1(Class ~.,data=Vehicle[sub, ],mfinal=mfinal, 
        maxdepth=maxdepth)
Vehicle.adaboost.pred <- predict.boosting(Vehicle.adaboost,newdata=Vehicle[-sub, ])
Vehicle.adaboost.pred[-1]


[Package adabag version 1.1 Index]