# boosting

0th

Percentile

##### Applies the AdaBoost.M1 and SAMME algorithms to a data set

Fits the AdaBoost.M1 (Freund and Schapire, 1996) and SAMME (Zhu et al., 2009) algorithms using classification trees as single classifiers.

Keywords
classif, tree
##### Usage
boosting(formula, data, boos = TRUE, mfinal = 100, coeflearn = 'Breiman',
control,...)
##### Arguments
formula

a formula, as in the lm function.

data

a data frame in which to interpret the variables named in formula.

boos

if TRUE (by default), a bootstrap sample of the training set is drawn using the weights for each observation on that iteration. If FALSE, every observation is used with its weights.

mfinal

an integer, the number of iterations for which boosting is run or the number of trees to use. Defaults to mfinal=100 iterations.

coeflearn

if 'Breiman'(by default), alpha=1/2ln((1-err)/err) is used. If 'Freund' alpha=ln((1-err)/err) is used. In both cases the AdaBoost.M1 algorithm is used and alpha is the weight updating coefficient. On the other hand, if coeflearn is 'Zhu' the SAMME algorithm is implemented with alpha=ln((1-err)/err)+ ln(nclasses-1).

control

options that control details of the rpart algorithm. See rpart.control for more details.

...

further arguments passed to or from other methods.

##### Details

AdaBoost.M1 and SAMME are simple generalizations of AdaBoost for more than two classes. In AdaBoost-SAMME the individual trees are required to have an error lower than 1-1/nclasses instead of 1/2 of the AdaBoost.M1

##### Value

An object of class boosting, which is a list with the following components:

formula

the formula used.

trees

the trees grown along the iterations.

weights

a vector with the weighting of the trees of all iterations.

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.

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 a tree and the weight of this tree.

##### 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''. In 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.

predict.boosting, boosting.cv

• boosting
##### Examples
# NOT RUN {
## rpart library should be loaded
data(iris)
iris.adaboost <- boosting(Species~., data=iris, boos=TRUE, mfinal=3)

## Data Vehicle (four classes)
library(mlbench)
data(Vehicle)
l <- length(Vehicle[,1])
sub <- sample(1:l,2*l/3)
mfinal <- 3
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$Class[-sub]) error.rpart <- 1-(sum(diag(tb))/sum(tb)) tb error.rpart Vehicle.adaboost <- boosting(Class ~.,data=Vehicle[sub, ],mfinal=mfinal, coeflearn="Zhu", control=rpart.control(maxdepth=maxdepth)) Vehicle.adaboost.pred <- predict.boosting(Vehicle.adaboost,newdata=Vehicle[-sub, ]) Vehicle.adaboost.pred$confusion

#comparing error evolution in training and test set