I am currently trying to implement the boosting model in R and while searching about it I have found the formula of bagging model.
boosting(formula, data, boos , mfinal , coeflearn , control)
There are six attributes in the model
as in the lm function.
a data frame, help to interpret the variables named in the formula
an integer, the number of iterations for which boosting is run or the number of trees to use. Defaults to mfinal=100 iterations.
controls details of the rpart algorithm.
I want to know the values of boss and coeflearn argument and how these values affect the performance of boosting model.