bagging {adabag} | R Documentation |

## Applies the Bagging algorithm to a data set

### Description

Fits the Bagging algorithm proposed by Breiman in 1996 using classification trees as single classifiers.

### Usage

```
bagging(formula, data, mfinal = 100, control, par=FALSE,...)
```

### Arguments

`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. |

### Details

Unlike boosting, individual classifiers are independent among them in bagging

### Value

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. |

### 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. (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.

### See Also

### Examples

```
## 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
```

*adabag*version 5.0 Index]