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bst (version 0.3-2)

mada: Multi-class AdaBoost

Description

One-vs-all multi-class AdaBoost

Usage

mada(xtr, ytr, xte=NULL, yte=NULL, mstop=50, nu=0.1, interaction.depth=1)

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.

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.

See Also

cv.mada for cross-validated stopping iteration.