predict.bagging {adabag} | R Documentation |
Classifies a dataframe using a fitted bagging object.
## S3 method for class 'bagging': predict(object, newdata, ...)
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 . |
... |
further arguments passed to or from other methods. |
An object of class predict.bagging
, 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.
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.
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.bagging <- bagging(Especies ~ ., data=iris[sub,], mfinal=10) iris.predbagging<- predict.bagging(iris.bagging, newdata=iris[-sub,]) ## 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=25, maxdepth=3) BC.bagging.pred <- predict.bagging(BC.bagging,newdata=BreastCancer[-sub,-1]) BC.bagging.pred[-1] # Data Vehicle (four classes) library(rpart) library(mlbench) data(Vehicle) l <- length(Vehicle[,1]) sub <- sample(1:l,2*l/3) Vehicle.bagging <- bagging(Class ~.,data=Vehicle[sub, ],mfinal=50, maxdepth=5) Vehicle.bagging.pred <- predict.bagging(Vehicle.bagging,newdata=Vehicle[-sub, ]) Vehicle.bagging.pred[-1]