What should be the value of boos and coeflearn argument in Ada boosting?

r
boosting

#1

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

Formula
data
boos
mfinal
coeflearn
control

formula
as in the lm function.

data
a data frame, help to interpret the variables named in the formula

mfinal
an integer, the number of iterations for which boosting is run or the number of trees to use. Defaults to mfinal=100 iterations.

control
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.


#2

@harry-
boos
if TRUE (by default), a bootstrap sample of the training set is drawn using the weights for each observation on that iteration. If FALSE, every observation is used with its weights.

coeflearn
if ‘Breiman’(by default), alpha=1/2ln((1-err)/err) is used. If ‘Freund’ alpha=ln((1-err)/err) is used. In both cases, the boosting algorithm is used and alpha is the weight updating coefficient. On the other hand, if coeflearn is ‘Zhu’ the same algorithm is implemented with alpha=ln((1-err)/err)+ ln(nclasses-1).

Hope this helps!

Regards,
Hinduja