bagging: Ensemble bagging classifier for multinomial classification

In bagRboostR: Ensemble bagging and boosting classifiers

Description

Makes multiple predictions on test set using resampled variations of data set. Uses randomForest as the weak classifier and expects a multinomial character outcome variable. Returns a final test set prediction with each forest having an equal vote. Implementation of Breiman's ensemble bagging classifier as described by Obitz & Maclin (1999).

Usage

bagging(formula, data, test, m = 5, ntree = 500, mtry = NULL, trace = T)

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

Value

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

Author(s)

Shannon Rush [e-mail]

References

Breiman, L. (1996) "Bagging Predictors", Machine Learning, 26, 123-140.

Opitz, D. and Maclin, R. (1999) "Popular Ensemble Methods: An Empirical Study", Journal of Artificial Intelligence Research, 11, 169-198. http://www.d.umn.edu/~rmaclin/publications/opitz-jair99.pdf

See Also

randomForest

bagRboostR documentation built on May 2, 2019, 11:12 a.m.