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.
Value
A list contains variable selected xselect and training and testing error err.tr, err.te.
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.