Applies the Bagging algorithm to a data set
Fits the Bagging algorithm proposed by Breiman in 1996 using classification trees as single classifiers.
bagging(formula, data, mfinal = 100, control, par=FALSE,...)
formula |
a formula, as in the |
data |
a data frame in which to interpret the variables named in the |
mfinal |
an integer, the number of iterations for which boosting is run
or the number of trees to use. Defaults to |
control |
options that control details of the rpart algorithm. See rpart.control for more details. |
par |
if |
... |
further arguments passed to or from other methods. |
Unlike boosting, individual classifiers are independent among them in bagging
An object of class bagging, which is a list with the following components:
formula |
the formula used. |
trees |
the trees grown along the iterations. |
votes |
a matrix describing, for each observation, the number of trees that assigned it to each class. |
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. |
samples |
the bootstrap samples used along the iterations. |
importance |
returns the relative importance of each variable in the classification task. This measure takes into account the gain of the Gini index given by a variable in each tree. |
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. (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. (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.
## rpart library should be loaded #This example has been hidden to fulfill execution time <5s #library(rpart) #data(iris) #iris.bagging <- bagging(Species~., data=iris, mfinal=10) # 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=5, control=rpart.control(maxdepth=5, minsplit=15)) #Using the pruning option Vehicle.bagging.pred <- predict.bagging(Vehicle.bagging,newdata=Vehicle[-sub, ], newmfinal=3) Vehicle.bagging.pred$confusion Vehicle.bagging.pred$error
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