predict.boosting {adabag} | R Documentation |

## Predicts from a fitted boosting object

### Description

Classifies a dataframe using a fitted boosting object.

### Usage

```
## S3 method for class 'boosting'
predict(object, newdata, newmfinal=length(object$trees), ...)
```

### Arguments

`object` |
fitted model object of class |

`newdata` |
data frame containing the values at which predictions are required. The predictors referred
to in the right side of |

`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 |

`...` |
further arguments passed to or from other methods. |

### Value

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 |

`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. |

### Author(s)

Esteban Alfaro-Cortes Esteban.Alfaro@uclm.es, Matias Gamez-Martinez Matias.Gamez@uclm.es and Noelia Garcia-Rubio Noelia.Garcia@uclm.es

### 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.

### See Also

### Examples

```
## 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
```

*adabag*version 5.0 Index]