predict.boosting {adabag} | R Documentation |
Classifies a dataframe using a fitted adaboost.M1 object.
## S3 method for class 'boosting': predict(object, newdata, ...)
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. |
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. |
Esteban Alfaro Cortes Esteban.Alfaro@uclm.es, Matias Gamez Martinez Matias.Gamez@uclm.es and Noelia Garcia Rubio Noelia.Garcia@uclm.es
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.
## 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]