predict.boosting: Predicts from a fitted boosting object

Description

Classifies a dataframe using a fitted boosting object.

Usage

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

Arguments

object

fitted model object of class boosting. This is assumed to be the result of some function that produces an object with the same named components as that returned by the boosting 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 boosting object to be used in the prediction. This argument allows the user to prune the ensemble. By default all the trees in object are used

...

further arguments passed to or from other methods.

Value

An object of class predict.boosting, 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, weighting each tree by its alpha coefficient.

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.

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

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.

Zhu, J., Zou, H., Rosset, S. and Hastie, T. (2009): ``Multi-class AdaBoost''. Statistics and Its Interface, 2, pp. 349--360.

Examples

Run this code

# NOT RUN {
## rpart library should be loaded
#This example has been hidden to fulfill execution time <5s 
#library(rpart)
#data(iris)
#sub <- c(sample(1:50, 25), sample(51:100, 25), sample(101:150, 25))
#iris.adaboost <- boosting(Species ~ ., data=iris[sub,], mfinal=10)
#iris.predboosting<- predict.boosting(iris.adaboost, newdata=iris[-sub,])
#iris.predboosting$prob

## 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 <- boosting(Class ~.,data=BreastCancer[,-1],mfinal=20, coeflearn="Freund", 
boos=FALSE , control=rpart.control(maxdepth=3))

#Using the pruning option
BC.adaboost.pred <- predict.boosting(BC.adaboost,newdata=BreastCancer[-sub,-1], newmfinal=10)
BC.adaboost.pred$confusion
BC.adaboost.pred$error




# }