A list contains variable selected xselect and training and testing error err.tr, err.te.
Arguments
xtr
training data matrix containing the predictor variables in the model.
ytr
training vector of responses. ytr must be integers from 1 to C, for C class problem.
xte
test data matrix containing the predictor variables in the model.
yte
test vector of responses. yte must be integers from 1 to C, for C class problem.
mstop
number of boosting iteration.
nu
a small number (between 0 and 1) defining the step size or shrinkage parameter.
interaction.depth
used in gbm to specify the depth of trees.
Author
Zhu Wang
Details
For a C-class problem (C > 2), each class is separately compared against all other classes with AdaBoost, and C functions are estimated to represent confidence for each class. The classification rule is to assign the class with the largest estimate.