# Ensemble boosting classifier for multinomial classification

### Description

Makes multiple predictions on the test set using samples chosen from the data set based on past performance. Uses randomForest as the weak classifier and expects a multinomial character outcome variable. Makes a final prediction on the test set based on votes by the classifiers weighted by performance. Implementation of Zhu et al.'s Stagewise Additive Modeling using a Multi-class Exponential loss function (2006).

### Usage

1 |

### Arguments

`formula` |
A formula expression for the classifier model. Expects the form outcome ~ predictors with a single outcome variable. The outcome variable is expected to be of type character. |

`data` |
A data frame consisting of the training data set. Must include all variables described in the formula including the outcome variable. |

`test` |
A data frame consisting of the test set to be predicted. Must include all predictor variables. May include outcome variable. |

`m` |
Number of classifiers to vote on final prediction. Defaults to 5. |

`trace` |
Setting for randomForest do.trace. Defaults to TRUE. |

`ntree` |
Number of trees for each randomForest to grow. Defaults to 500. |

`mtry` |
Number of variables randomForest randomly samples as candidates at each split. Defaults to the square root of the number of variables in data. |

### Value

Character vector consisting of a final prediction for each test set sample.

### Author(s)

Shannon Rush shannonmrush@gmail.com

### References

Zhu, J., Rosset, S., Zou, H. and Hastie, T. (2005). Multiclass AdaBoost. Technical report, Stanford Univ. http://www.stanford.edu/~hastie/Papers/samme.pdf

### See Also

`randomForest`