predict.boosting: 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:
formulathe formula used.
votesa matrix describing, for each observation, the number of trees that assigned it to each class, weighting each tree by its alpha coefficient.
classthe class predicted by the ensemble classifier.
confusionthe confusion matrix which compares the real class with the predicted one.
errorreturns the average error.

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.

Examples

Run this code

## 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[sub,-1],mfinal=25, control=rpart.control(maxdepth=3))
BC.adaboost.pred <- predict.boosting(BC.adaboost,newdata=BreastCancer[-sub,-1])
BC.adaboost.pred$confusion
BC.adaboost.pred$error


## 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, 
	control=rpart.control(maxdepth=maxdepth))
Vehicle.adaboost.pred <- predict.boosting(Vehicle.adaboost,newdata=Vehicle[-sub, ])
Vehicle.adaboost.pred$confusion
Vehicle.adaboost.pred$error