mada {bst} | R Documentation |
One-vs-all multi-class AdaBoost
mada(xtr, ytr, xte=NULL, yte=NULL, mstop=50, nu=0.1, interaction.depth=1)
xtr |
training data matrix containing the predictor variables in the model. |
ytr |
training vector of responses. |
xte |
test data matrix containing the predictor variables in the model. |
yte |
test vector of responses. |
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. |
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.
A list contains variable selected xselect
and training and testing error err.tr, err.te
.
Zhu Wang
cv.mada
for cross-validated stopping iteration.
data(iris) mada(xtr=iris[,-5], ytr=iris[,5])