# Predicts from a fitted boosting object Classifies a dataframe using a fitted boosting object. ```r ## S3 method for class '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. ## Returns 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. ## Author(s) Esteban Alfaro-Cortes Esteban.Alfaro@uclm.es , Matias Gamez-Martinez Matias.Gamez@uclm.es and Noelia Garcia-Rubio Noelia.Garcia@uclm.es ## See Also `boosting`, `boosting.cv` ## Examples ```r ## 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$Class[-sub]) error.rpart <- 1-(sum(diag(tb))/sum(tb)) tb error.rpart BC.adaboost <- boosting(Class ~.,data=BreastCancer[,-1],mfinal=10, coeflearn="Freund", boos=FALSE , control=rpart.control(maxdepth=3)) #Using the pruning option BC.adaboost.pred <- predict.boosting(BC.adaboost,newdata=BreastCancer[-sub,-1], newmfinal=10) BC.adaboost.pred$confusion BC.adaboost.pred$error ```

predict.boosting function